{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is for working with census tracts and precincts.  Copied from TX 30Jan\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/oh_pl2020_t.dbf\") #for Texas, need only this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8590a2a-0527-45ab-a4b3-fe0fdaa45be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>153</td>\n",
       "      <td>533102</td>\n",
       "      <td>39153533102</td>\n",
       "      <td>5331.02</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5924402</td>\n",
       "      <td>225599</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-81.44045 41.25459, -81.43934 41.254...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>153</td>\n",
       "      <td>533200</td>\n",
       "      <td>39153533200</td>\n",
       "      <td>5332</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>16548913</td>\n",
       "      <td>79769</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-81.44044 41.21441, -81.44044 41.217...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>029</td>\n",
       "      <td>951600</td>\n",
       "      <td>39029951600</td>\n",
       "      <td>9516</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>96544091</td>\n",
       "      <td>950949</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-80.63997 40.74541, -80.63996 40.745...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>093</td>\n",
       "      <td>097100</td>\n",
       "      <td>39093097100</td>\n",
       "      <td>971</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>154468293</td>\n",
       "      <td>927233</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-82.34128 41.18795, -82.34123 41.188...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>093</td>\n",
       "      <td>010200</td>\n",
       "      <td>39093010200</td>\n",
       "      <td>102</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>4337885</td>\n",
       "      <td>1552203</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-82.01893 41.49378, -82.01893 41.494...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        39        153    533102  39153533102  5331.02  Census Tract   G5020   \n",
       "1        39        153    533200  39153533200     5332  Census Tract   G5020   \n",
       "2        39        029    951600  39029951600     9516  Census Tract   G5020   \n",
       "3        39        093    097100  39093097100      971  Census Tract   G5020   \n",
       "4        39        093    010200  39093010200      102  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S    5924402    225599  ...        0        0        0        0   \n",
       "1          S   16548913     79769  ...        0        0        0        0   \n",
       "2          S   96544091    950949  ...      150        0        4      146   \n",
       "3          S  154468293    927233  ...        0        0        0        0   \n",
       "4          S    4337885   1552203  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        4        0        0        4   \n",
       "1        0        1        0        0        1   \n",
       "2        0        2        0        0        2   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-81.44045 41.25459, -81.43934 41.254...  \n",
       "1  POLYGON ((-81.44044 41.21441, -81.44044 41.217...  \n",
       "2  POLYGON ((-80.63997 40.74541, -80.63996 40.745...  \n",
       "3  POLYGON ((-82.34128 41.18795, -82.34123 41.188...  \n",
       "4  POLYGON ((-82.01893 41.49378, -82.01893 41.494...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d76e7d7-6979-475e-bc20-36f9f1c09e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3168"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tractPopFile)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 3168 popn tracts for OH\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "# see \"grabOHtract\" for alternate methods to marry data from multiple files\n",
    "STATE = \"OH\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0020002']\n",
    "tractBlack = tractPopFile['P0010004']\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population OH\n",
      "state pop= 11799448 pct Hispanic is  0.04418071082647256\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE)\n",
    "print(\"state pop=\",np.sum(tractPop), \"pct Hispanic is \",np.sum(tractHisp)/np.sum(tractPop) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,15000]\n",
    "y = [0,15000]\n",
    "plt.plot(tractPop, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Black population by total population OH\n",
      "state pop= 11799448 pct Black is  0.1253262864500102\n"
     ]
    },
    {
     "data": {
      "image/png": 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SIukMLBeR+d7zY4E5IjIDQFXPS1XljDEpEvDDX8fBnk1uHOSEn0BWi3TXyjRSiQSSSSmvhTGmYezZBO16uiSLZ/8GOvazVO+m3hKZ/vtxQ1TEGJNCgQAseApm3Q9n3O9mZFmOLJMkiU7/NcY0VsVfwYwfw8a5MGis7Zluks4CiTFNWeHzLslii9Zw/pPw7Qm2Ot0kXY2ztkRkZJSy76emOsaYpOo0AIaOg1sWwDGXWxAxKZHI9N+nROTo4BMRuRz4n9RVyRhTZxUH3TjIrPvd8wEnw6UvuOm9xqRIIoHkYuB5ETlSRG4AbgLOTG21jDG1tmEuPHkSfPYw7N/hkiwa0wASmbW1RkQmAG8AG4EzVfVAqitmjElQWSl8MBnmPwUd+8BV02Dw2HTXyjQjMVskIrJERBaLyGLgNaAT0B+Y55XVmYj8VESWichSEXlZRFqJSCcReV9EvvZ+54adf7eIrBKRlSJyVlj5SK+eq0TkUW9fd2Oal72b3c6Fx/0QbpxjQcQ0uHgtknNT8YYi0hv4CXCUqh7w9mqfABwFfKCqD4nIXbgdGO8UkaO848OAXsAsERni7en+BDARmAu8BYzj0J7uxjRd+3fBsmlw7PXQdahLsmg7Fpo0idkiUdX1qroe6AnsCnu+C6jvv9hsoLWIZANtgM3AeOB57/jzwPne4/HAK6papqprgVXAKBHpCbRX1TleQskXwq4xpmlSdVl6Hx8Fb98JO7525RZETBolMtj+BPBN2PN9XlmdqOom4HfABmALsEdV3wO6q+oW75wtQHCaSW/c2ExQkVfW23scWV6NiEwUkQIRKSguLq5r1Y1Jr9KtLsHiP66B9r1h4keWZNFkhEQCiQRTyAOoaoB6LGT0xj7GAwNwXVVtReSqeJdEKdM45dULVaeoar6q5nft2rW2VTYm/QJ+eHYcrJoFZ0yG6z+AHkfXfJ0xDSCRgLBGRH7CoVbITcCaerzn6cBaVS0GEJFpwAnANhHpqapbvG6r7d75RbjU9UF5uK6wIu9xZLkxTceeImjXyyVZPOd30LE/dBmc7loZU0UiLZIf4W70m3A37+NwA9x1tQEYLSJtvFlWY4HlwAzgGu+ca4Dp3uMZwAQRaSkiA4DDgfle91epiIz2XufqsGuMadwCfpj7JPzpWCh4xpUNPt2CiMlIiawj2Y6bNZUUqjpPRF4DFgKVwOfAFOAwYKqIXIcLNpd45y/zZnZ96Z1/szdjC+BG4DmgNW62ls3YMo1f8UqYfgsUzYfBZ8CQcemukTFxidaw+lVEWgHX4abftgqWq+q1qa1aauTn52tBQUG6q2FMdAV/hbd/DjmHwbiH4FuXWn4skxFEpFBV86MdS6Rr62+46b5nAR/jxiJKk1c9Y0xI50FwxLlw83z49mUWREyjkMhg+2BVvURExqvq8yLyEvBuqitmTLNQcQA++hUgbsOpASe7H2MakURaJBXe790iMhzogEuVYoypj3X/hidOhH8/AmV7LcmiabQSaZFM8dZ+3IubQXUYto+7MXV3cC/Mus/NxsrtD1fPgIGnpLtWxtRZIrO2nvYefgwMTG11jGkGSrfCFy/B8bfAqb+AnLbprpEx9RIzkIjI7fEuVNWHk18dY5qofTtdksVRN0DXIXDbYttsyjQZ8Vok7RqsFsY0VaougLz1czi4Bwae6hYVWhAxTUjMQKKq9zdkRYxpcvZugX/dDivfgl7fgfEzbGW6aZLidW21Ai4DSoA3gZ8BJwOrgQdUdUeD1NCYxijgh7+eDaVb4MxfwnE3Qladc50ak9Hi/ct+ATf1ty3w38BS4E/ASbi0JCnZ+MqYRm33Bpfi3ZcF5/zezcrqPCjdtTImpeIFkqNUdbi3+VSRqgbnJ74jIosaoG7GNB4BP8x9Aj78pUvzftxE2/LWNBvxAkk5gKpWikhkenZ/lPONaZ62fQkzboFNhS7B4hHnpLtGxjSoeIEkT0QexW0gFXyM9zzqToTGNDsLnnFb3rZqDxc9A8MvsvxYptmJF0h+FvY4Ml2upc81zZuqCxhdh8Kw812m3rZd0l0rY9Ii3vTf5xuyIsY0CuX7YfaDbjD9jMnQ/yT3Y0wzlkjSRmMMwNpP4YkTYM6foHyfJVk0xmMT242pycE98P4kKHwOcgfANW9aqndjwtTYIhGRExMpM6bJKt0Gi6fCCT+GG/9jQcSYCIl0bT2WYFnCRKSjiLwmIitEZLmIHC8inUTkfRH52vudG3b+3SKySkRWishZYeUjRWSJd+xREZsuY5Jk3w6Y9xf3uOsQuG2JW6Ge0ya99TImA8VLkXI8cALQNSITcHsgq57v+wjwjqpeLCI5QBvgF8AHqvqQiNwF3AXcKSJHARNwe8b3AmaJyBBV9QNPABOBucBbwDjg7XrWzTRnqrDkNbdvelkpDBrr8mPZjCxjYorXIsnBbWKVjcsEHPzZC1xc1zcUkfa4nF3PAKhquaruBsYDwZlizwPne4/HA6+oapmqrgVWAaNEpCfQXlXnqKriUroErzGm9vYUwUuXwbTrodNA+NGnlmTRmATEm/77MfCxiDynquuT+J4DgWLgryLybaAQuBXorqpbvPfeIiLBPNu9cS2OoCKvrMJ7HFlejYhMxLVc6Nu3b/I+iWk6/JXw3DnwzXY461dw3A/dFF9jTI0SGSN5WkQ6Bp+ISK6IvFuP98wGRgBPqOp3gH24bqxYoo17aJzy6oWqU1Q1X1Xzu3btWtv6mqasZL3Lk5WVDef+0Q2mH3+TBRFjaiGRQNLF63oCQFVLgPrsylOESwI5z3v+Gi6wbPO6q/B+bw87v0/Y9XnAZq88L0q5MTXzV8K/H4XHR8ECbzfpQadCpwHprZcxjVAigSQgIqH+IBHpR4xv/olQ1a3ARhEZ6hWNBb4EZgDXeGXXANO9xzOACSLSUkQGAIcD871usFIRGe3N1ro67BpjYtu6FJ45Hd6/1w2mH3leumtkTKOWyILEe4DPRORj7/nJeOMN9fBj4EVvxtYa4Ae4oDZVRK4DNgCXAKjqMhGZigs2lcDN3owtgBtxe6O0xs3WshlbJr75T8E7d0GrjnDxX2HYBZZk0Zh6Ek0gzYOIdAFG48Yl5jTm3RHz8/O1oMByTjY7wSSL6/4NC593A+ptO6e7VsY0GiJSqKr50Y4lmiLFjxuzaAUcJSKo6ifJqqAxKVO+z2025ctyCwr7n+h+jDFJU2MgEZHrcdNz84AvcC2TOcBpKa2ZMfW15iOY8RPYvR5G/fBQq8QYk1SJDLbfChwLrFfVU4Hv4NaBGJOZDuyG6bfAC+PBlw0/eBu+9xsLIsakSCJdWwdV9aCIICItVXVF2IwrYzLPvmJYOg1OvA3G3AUtWqe7RsY0aYkEkiJvQeIbwPsiUoKt1zCZ5pvtsPR1GH0jdDncJVm0wXRjGkSNgURVL/Ae3icis4EOwDsprZUxiVJ1Kd7fudMNrB9+JnQeZEHEmAYUN5CIiA9YrKrDIZR/y5jMsHsjzPwprHof8kbB+D+5IGKMaVBxA4mqBkRkkYj0VdUNDVUpY2oUTLK4bwec/Rs49nrLj2VMmiQyRtITWCYi83EJFgFQVcsrYRrerrXQsa9Lsnjeo27r29x+6a6VMc1aIoHk/pTXwpia+CthzmMw+1dwxmQY/SMYOCbuJYXrS5i7ZiejB3ZmZL/cuOcaY+oukUDyPVW9M7xARH4N2HiJaRhbFsOMW2DLIjjiXBh2fo2XFK4v4cqn51JeGSAn28eL14+2YGJMiiSyIPGMKGVnJ7sixkQ1bwo8dSrs3QKXvgATXoR2PWq8bO6anZRXBggoVFQGmLtmZ5XjhetLeHz2KgrXl6Sq5sY0G/H2bL8RuAkYKCKLww61A/6d6oqZZi6YzqT7MDj6UjjrQWjTKeHLRw/sTE62j4rKAC2yfYweeGg6cE2tFesSM6Z24nVtvYRLy/4rqu5gWKqqu1JaK9N8lX0DHz7gUpuc9WCdkyyO7JfLi9ePjhoQorVWgsetS8yY2ou3Z/seYA9wecNVxzRrqz6AN2+DPRvdnum1SLIYrRUxsl9u1CAQr7USL8gYY6JLNI28MalzoATevQe+eBE6H+6SLPY7PuHLa9uKiNdaiRdkjDHRWSAx6bdvB3w5HU66HU65E1q0qtXldWlFxGqtxAsyxpjoEpm1lRIikiUin4vITO95JxF5X0S+9n7nhp17t4isEpGVInJWWPlIEVniHXvU27vdNAal22DO4+5xMMni6f9b6yACh1oRWULCrYh4s7ZG9svl5lMHWxAxJkHpbJHcCiwH2nvP7wI+UNWHROQu7/mdInIUMAEYBvQCZonIEG/f9idw+8fPBd4CxmH7tmc2VVj0MrxzN1QcgCHjXH6sWszIilTbVkRkV9ikc4dRsr886rU2g8uYmqUlkIhIHnAO8CBwu1c8HhjjPX4e+Ai40yt/RVXLgLUisgoYJSLrgPaqOsd7zReA87FAkrlK1sPM22D1h9BnNJz3WI1JFhO9kcfqqoomvCusrCLAvW8sQaHa+IrN4DImMelqkfwR+DluTUpQd1XdAqCqW0Skm1feG9fiCCryyiq8x5Hl1YjIRFzLhb59+yah+qbW/JXw/Lmwfxd873eQfx2FG/cwd/aqmEHipXkbmDR9Kf6A0iJLuCS/DxeOyKv3zXz0wM5kZ/korwyggF9deeT4is3gMiYxDT5GIiLnAttVtTDRS6KUaZzy6oWqU1Q1X1Xzu3btmuDbmqTYuRoCfpdkcfzjcNMcGHUDhRv3cOXTc/n9eyu58um51cYqCteXMGn6UioDigLlfuWleRuinltbI/vlcvHIvCr/gITq4yvhYy9ZPmHz7gO2Et6YKNIx2H4icJ7XNfUKcJqI/B3YJiI9Abzf273zi4A+Ydfn4XZoLPIeR5abTOCvgE9+B38eDfOfcmUDTnaZe6k5hcncNTsJaNXvBUr0c+viohF5tGzhgkROto/Lj+tbresqOPZy2ai+IMLL85MTyIxpahq8a0tV7wbuBhCRMcAdqnqViPwWuAZ4yPs93btkBvCSiDyMG2w/HJivqn4RKRWR0cA84GrgsYb8LCaGzV+4JItbl8BR58PwC6udktsmB58IoFFnWgVbA+WVAQTw+YRAIPq5dZHoAP3IfrnMXbOTSr91cRkTSyatI3kImCoi1wEbgEsAVHWZiEwFvgQqgZu9GVsANwLPAa1xg+w20J5uc5+Ed38BbbvAZX+HI79f7ZTC9SVMnrkMf0DJ8gmTzh1W7cYceaMHkj57KnyAPt6gfl0WKUZ7PZsBZpoqUY06rNBk5efna0FBQbqr0fR46UxWznuXwOd/p+y0BzhmSP+oN8/HZ6/i9++tJKCQJXDakd05WOHn7OE9ueK4hp8MkcjsrNoEgWivB9gMMNOoiUihquZHO5ZJLRLTGJWVwqz7IbslhUfcwZUzA5RXTiDnb8uZdK6PyTOXVbuhbt59gGyf4A8oIvD+l9sA+PTrHWzYuY92rVs06Lf2WOM14YEjcnpxvMAS6/WCZeXWPWaaGAskpu6+nuXWhewpgtE3MXf1jio30LeXbqmyXuPJj1fz6dfFlFcGyM7ycdmoPizbtIdFRXtCLznl0zVA9TUdkLquociuq9w2OTWmmY/W4gjWLVpX2MqtpQS8xn9A3RiRMU2FBRJTe/t3uXGQRS9Dl6Fw3XvQZxSj15eQM3tV6AZ69vCezFuzk3K/m8L74YrtqGoo0ABcdmxfFhUtCb20atXZWcEbeHBNSUA16V1DkeMxNa0fiTw+bWERry8sqhJYIgfy567ZiZta4KZKluwvT0rdjckEFkhM7e3fBctnwsk/h5PvgOyWQPQB8iN7tg+1ONTrygJ3Q32tsIiLRuTxfxcczdtLtzCsZ3uem7Ou2qB2+JoSSE3XUGTXVbxNscK75lpk+9w6l4jAE5mra/TAzrRsYVmFTdNkgcQkpnQrLJ4KJ/wYugyGny6hcDvM/XRj1D1Awrt/wH0Lz84S+nZuy6rt3wDg9x+66QYH2c8Y1qNa99XcNTvxBw5NChERNnmLA1MxzhBranD4Zwp2zV00wi1lmrawKG6QqEs+MJvhZRoLCyQmPlX4/O9uvxB/GRxxDnQeROH2+LOQwrt/BOjYtgW791ew2gsiAFlZ1W+60XJmBb/Nl1cEEJ8gKC/P28Cr8zcw9sju/PCUQaHglYybb6zXCf9Mfn+A3h1bh44nuiYlkXpZji/T2FggMbGVrIM3b4U1H0G/E+H7j4aSLNY0jhC+oDCgsGtfRbWXDwQCCVUj/Nv8pt0HeHnehlCOrPe+3MZHK7dz33nDq80Qq8vNN95NPN56ktokjayJ5fgyjU3a9iMxGc5fSdkz51C+fgHrj/8lXDPTdWl5Yu0BEtznA9y39BMHd4n5FpUBeH1hUczj4YJ7hFw0Io8sX9U0axV+rTJDrD5pVOKlbgkGtNvPHBo3UMXb6yQRddlfxZh0shaJqWrnasjtT2FRKY/s+QGrK7ux7eMunFuyiJ37ykOLBqP1+Uf7Nn/b6UOYs3oHlTEaH5GZNyO7lSKfj+yXy+Txw7nXywoM0CJLOHt4Txas2+W6v0TqPL22plXs0Voe4XWE+i88tF0aTWNjgcQ4/gr47I/wyW/gjMnMPXAGn1Ue6dY+qPLGFy4f5qdf7wCIugI9/Nv8wYoAk99cxqTvD+OyY/vy4rwNANXyZl044lDezWgbToV3V4VvQDX1h8czbWERikvAGLzZBqcIT565jKE92qX8Jh5Z5wtH5CWlWyqZXWXGpJoFEgObFsKMH8O2pTD8Ihh+MbnLDkTPyQ+8vXQLQ3u0q/bNe/TAzmT7hHJvg49FRXu45Mn/MPG7A2kVNvU11o6Ekd1Kry7YQFlFIDS99t7pS0MB6OUbRvPgBUeHWgPg1mYEwtapNMRNPLLOQuypw8Y0VRZImru5T7jFhYd1hwkvwxHfCyVVjJWG7ezhPavtMjhtYREPXnA0l+T3CbU+wK3ifurTNTxw/tFVgkd4AAjetEMZgVXJyvKxbPOeUDALtozABZVp3thKZAumoW/ikV1hF47I48IRedYtZZoVCyTNlZdkcYVvMHtzz+HdXjfxvdZHMhI3AF5WUX1QI69jK47s1YGhPdzGluG7DP6jYGPoJvrK/A2hXQfBza5aunkP/3fB0UDV7iCfCJPHD2doj3ZMnrmMgCo+n3DKkK7M8nJwRa0+1bfMXbp5T0LdUslcoxGrK6whUrsYkykskDQ3B/fCrP+F7FYUHvlzLp9eQbn/cthcwnML53DDSQN4rbAoardW0e6DbNp9kE+/LubF60dz8ci8Q1NxAxpaXPjA+UfzP28sIWwNYWgVezBdSDAABFSZNH0plx3bJ1QGSrd2LcnySWg1e7gsIbQQMNiVpsCrCzYyvFcHbj710OyyaIP3ka2YaN1stRGvK8zWhJjmwAJJc/LVezDzNrR0C1/0vpJp+zdSEdZ08AeUv3y6JsaGxU4wD9brC4vc9rRZh1KFBLuSggPxj37wFVv3lgFQGTZmMXpgZ3wioR0QAwFle2lZlaSGw3p1YPL4DqE924N7K2f5XAsmeDMO70rzB1xQCg6yR7uJhwex8spAyvJ3BdmaENMcWCBpDvbthHfugiVTKW49kIll9/HFqsG0yCrC5wN/WC+WKvjELTDKyvLx7bwOLFhXdT2EiOvKqvQr2VnCZaP6hlobhetL+MvHq5m1fFuVFkmAQxlvg1N4J3mD5zktfHRr17JaUsObTx3M0B7tmLtmJ7ltcqK2HC4ckcerCzaGWi4B1dDNOtpNPHxMQ0RCQao2N/nadFXVZVOsurDuM5NOFkiauML1JSxfuojLV7zNl4Nv5MKlx1Ph/bWX+6M3PQIK387rwPEDO7Nsy95qx1vnZFN6sBJwiwEFQkHk8ilzor6ucGi218h+uVxxXN9QkAjeXIMZdH1h60Ci7QPy+OxVVXZNvP6kATz92dpQyyJ4LNpNPHxMI7dNDpNnLqv1zoe16apqiDUh1n1m0q3BA4mI9AFeAHrgvqhOUdVHRKQT8CrQH1gHXKqqJd41dwPXAX7gJ6r6rlc+kkNb7b4F3KrNbcvHWPZupuiT57ly3rcpr1Qeyf4jHbZ3pYJvar4WN3U3fJ+QcMEgElRc6rqv5nop46NR3BqUeWt2ct95w0Oti9EDO4fWg/zX8f15+rO1+APR14FUSZroExCh0u9unpPHu9csPVDBH2d9FVo4OencYby9dAtnD+9ZLbEkUCWYJXLzrUtXVarXhFj3mUm3dLRIKoH/VtWFItIOKBSR94H/Aj5Q1YdE5C7gLuBOETkKmAAMA3oBs0RkiLdv+xPARGAuLpCMo7nv267K+vefoNucX9JFK+he+RDrtQc7KlqzY3tiQSSaYLdTNCX7y3l89ipKD1TPpwXQqU0Ldu13x8r9yr1vLAm9VnD/EXCD6ME4dLAiEEqfEm2fEBew3MkVlQFK9peT2yaH3767Eji02+Jzc9ZRXhlgwbpdURcohmcrDrZ04q1cr21XVUN0OTVU95kxsTR4IFHVLcAW73GpiCwHegPjgTHeac8DHwF3euWvqGoZsFZEVgGjRGQd0F5V5wCIyAvA+TTnQLJrDXun3kS/rXOY4z+KuyqvZ732AOKOn9coS0B8QmWM1saCdSUsWFdSLd0JuNTx+f078V7YVN4YL1Ot/NUFG3itsCjU6ph07jCyfUKFN0srVD+fMHpgZya/uazK9W98sSmhb+rRZnIF17AM79WhWjLIRLuqEu1yqm+wsZQqJt3SOkYiIv2B7wDzgO5ekEFVt4hIN++03rgWR1CRV1bhPY4sb578lfD8eLL3FnN3xXW84j8VDcvJKbhB9Fg38Xh6dmhF0e6DNZ4X7aWDLY4WWS4QBdOjRDs3K6J+/oDLEBwcDF+6eQ+IEH614GZujeyXS7f2rYBD3XF9OrVh94GKGr+pR87kCs/jlSWu/vE2rYolkS6nZI1vWEoVk05pCyQichjwOnCbqu4VifZ91p0apUzjlEd7r4m4LjD69q2eI6oxW7q4gE92HMbeg8rGAzdQeKAjW6l+w8zywYi+uSxYV1Lr1kkiQSQWf0B5/8tt5GQJZxzVnY9WbidyqaNP4PQjuzNmaDfue3NZaDOsbB/4fD78fhcIBKgMm2ImQMsWbjV54foSt/bEC0bZPrjr7CMBavymHt41hDeTKyjgzWILTj2OFoxitSgS6XKy8Q3TFKQlkIhIC1wQeVFVp3nF20Skp9ca6Qls98qLgD5hl+cBm73yvCjl1ajqFGAKQH5+ftMYjK8s5+0nf8bY4r/xeuUV/NV/NjAo9ukBmB8xjTfS4K5tWbtzX5XpwMlS6VcOVPipDFsT8q28Dgzv3YELw5IuAjz777WgyrUnDWRoj3ahNSvDenUI3ZizsnxcPDIvtDCxys6FXnnkKvNYN/xg19C0hUW8suBQehdwAVhECPhdJoBI8VoU0bYejhyHsfEN0xSkY9aWAM8Ay1X14bBDM4BrgIe839PDyl8SkYdxg+2HA/NV1S8ipSIyGtc1djXwWAN9jLRaXjCb1m/dytmB9bwROIE3/Ccm5XWPG9iZ3LY51daNRNOhTTZ79lfWeF6QzycM69k+lD1YgcuOPTQFOCi8RTJp+hImjz+aad604Fgr0R+fvarKt/plm/aEAkxQTV1II/vlMm1hUZUg2rtjK8YM7cbL873V+/7qLYaaWhThg/nR3t/GN0xTkI4WyYnA/wOWiMgXXtkvcAFkqohcB2wALgFQ1WUiMhX4Ejfj62ZvxhbAjRya/vs2zWCg/Z9//gXnbfsz28nl2oo7+DAwIimv6xN4yUt3koiagsiRPdqxfGtp6Pn1Jw2gXesWVRYdzl65PbRyvWULHxd5KdiDKgOudXLQy/tV7s3OikyBsmn3AbJ8rtWguKnLlz81l/u+fyjoJNKFFPnZt+4tq9IKCrYY6jKLK9772/iGaezSMWvrM6KPbwCMjXHNg8CDUcoLgOHJq10GU+W2V79gw8au7M86jYcqL6eUNglf3q9TG8oq/aGUJZGipLSqsyxxA93hgaS0rJIzhvWgpZdOPssnfLhie2g8oqwiwNJNe/BJ1bqE7/EeUKpsWBX+LT/yH1RFZYB7vXxfLbKE+84bXuMN/6IRebwalnBSVSnZX16teyqyZZFIi6Ipd2HZqnpjK9sz3cE98P4kFhQd4I3144EhLKwcUuuXWb9rf/LrFoMqrCmuumZle2lZlW6czbsP8FJYunkFFhftIct36DV8vqoD3+DWrASFf8uPJhgQyv1aLTMwVB+vGNkvlwfOP7pK/q3wG/7KraVRt/RNZBZXU+3CslX1BiyQZLaVb1M+/Vay9xdTWHkOsSerpYYAR/RoR+nBilrN3ApAtay93dq1BA5140TrRgtuOTJhVF8EF3zCWy05WYdmTUV2aUXKbZvDrn2Hgk74n9rKraXV1oYEb37RUrcEb5ThH0mo/X7q9e3CitzSNxOCks06M2CBJCO9/skX5H5yL6dVfsKaQB9+XnE/izX2jKxUEWDltlJ8IjFXtscqX7dzv7vJh22pG34jXLa5evoVH+7mPLxXB+6bsbRaupVj+nQEqn4LjjWoEx5EcrJ9DOvVgSufnktZRQBv76yYyRrDb/jhA/lVPrfApHOHNdhNM156mHS2Appyl51JnAWSDHPbK5/z+aKFvJlTwMOVF/OE/7xQksWGFoBQMyF48w0SgSO6t2NF2DhIpAGd29CzY2vOHt4ToMqNMNoM427tW/KtvI7MXrk9as6u+etKuPypuYwZ0jW0BW88PoETB3fhttOHMHfNztA1wc/hk5pbFcEbZbRgEt7NBskZK4j1GlW++XvpYWqbtTgVmmqXnakdCyQZYvGyZSx5ewpv7DgD6MGJZY/WajA9FbK8ge9sr0vp0693oLiFeYKyYmtp3Jv52p37WbNjH/PW7uLIHu2q3AijXbd1bxlb4+yKCO7G+cHybTFbR3CoA9AnEgpiizburtJ8Eg4FGYBLn/wPq4q/YVDXw7jr7COrrQWZtrCIr7eVUrjeLeiMHD9JxlhBvNcI/+af5bVIggs1090KsFlnxgJJugUCrH//cQb+50EGE2CKfIv12iPtQQQO9Rr5VfnEW/8BMKJvRwpirJDPEteSUSU0tlFeGQhlEnYrxN3U3qB4CSEjicSeYRYMckO6HcZX27/BH1Due9Nt3xueJyy4Ij4URP7yn9D6kV37Srj0yf8w9UcnVLk5BlPcZ2f5OGVI19CYT1AyxgpqmiIcOVnAWgEmU1ggSaOlixeS+8Ed9NtTyGeBYdxdeT0btXvK37emqcBBwRt25Er3dTv2VbvxZ2cJl+b3YUdpGe/XsNd69w6t2VRyoEpZovL75bJ4056YXVv+gFaZdhy+LiXopMNdS2Rkv1wen72q2ufzK6GbeOH6Ev4466vQ+1VUBvhwxXYCAeUfhUWhtSq5bXLqNFbw0rwNoTT3NY03RH7zb0wBxKYIN23S3LbvyM/P14KCgrTWoXB9Cf8sXMePFl1Me/bzQOVV/MN/Cg05IyuWFj6hopaLSgZ3bctxAzszrFcH/nfG0irb9ybbqP65HN69HfPW7GRV8b5aX5/jrSlZunlPKO1K5P7yWQJTf3QCQGiAPtYnyvIJqlqlpXKht6o+2o2zcH1JaP+V9i2zefKTNaFj/3fB0bXeH6UxsCnCTYOIFKpqfrRj1iJpYMsWLeCqqVs54PexUm5ivXZnO5nzn6q2QQRgVfG+Ot3U62L+uhLmryshO0uqZQsW7ydWqrDeua3p3aFVlcDRIkuqhe8Hzj861Fopr4w/qB/efTfry220bOFmiEWbXvzSvA1VMgtHvu/bS7eE9rt/fWER0xYWxQ1KmS7YCtm8+4BNEW7iLJA0lMoy+PT3DP34d1zOFTzL2SzQI1L2djlZEnO3wsYmcrU7uCSQeR2rprev6dNuKjlQpUsNqNZ6EmDp5j08PntVqLsq3qLHcIoLKFM+WV3txgmE0sHEqu/Zw3tW2654asFGl/U4oI3q23zkdOXsLF/GTA4wyWeBpCFsXAAzboHiFbzp/y7T/Cel/C0bSxCpaaDdJzCoS1u+jtLiibZIsrafukWWEFA9NE4i7uYd8G7cwSSRX28rZfoXm2t8/YC6NTTBugdvnHPX7CQQ0Y2ckyVce+IAlm3ZG9oa+PHZq6oEtwq/hv6Mwr/NZ/qYQ/jEAX9AuWxUH3p3bJ1x9c30P8fGwgJJqv3nMXjvXsrb9uSRLr/k8aKB6a5RRknkxhwtiCTDqP65dGyTwwfLD00OUCU0wyu4he/ogZ15+P2vaqxrpzYtKNlfEUpKGZxePLJfLiu3uoWdqm5zr9OO6MaPThlUbfxk8+4DVWa1Bbve/N7CzmDSyOC3fZ8Ik8cPD3WJJUMybq6REwcuitgqIBPY2E3yWCBJlUAAfD7IG8X2I67k7GWnsXNnq3TXyoSJt8mX4PYhyW2Tw18+Xl0t51c0wX3pBZcn7OzhPUOth8kzl7nXEDd9OloQCd9T5YwjYg/ch6+2D6gyafrSqPvR10Uyd2zM9IWKlt4leSyQJNuB3fDePdCiDXzvt9D3OP6xujO7vliZ7pqZCPFCg4hrBdwbMaMrUQFVJs9cFpqFFZr5pS6AXT5lDi9PPD5046rSFeQPcEyfjlXS5UfuvChhqQYCAa31TTChFfRRbq61aa3EW6iYji6lyPe09C7JY4EkmZbPhH/9N+wrhhNvdf/Rxa0Kz/JJtUSGJnOF1tDU4a8smIYleCPObZNTLcVMhV+rjHds2n0g4QHplVtLCZ+2n50VfQvgoOANNLdNTmjNS6yklfFurslqraSjSynWe2Z6q6mxsECSDN8Uw1t3wJdvQI+j4YpXodcxocMj++Vy/UkD+Msna2o9GGwah2gzy3w+4YuNu/n4q+KorZrcNjnVZjdNGNW32tbD4QrXl3hp7t1zAS7J7xP3/PBZYD5xqWP8gei5uuLdXBPpCkqkpZGOLqVY72npXZLDAkkylO2FNbPhtHtdSySrRZXDhetLeG7OOgsiTVi0QOH3a8xV/gpMmrGUU4d2C3V7+QNKr46tgep7pQS9vrCoSss2yycM69WhyvnhN/PXFxZVmcEX8PrXgoEvyyfVWh2xAsHogZ3J9gkVfq12XfDa4ALOLF/sSQC16VJKVheYdWOllgWSutq9ERa/At+9AzoPgp8ug5btop4a/DZkmpea/sYrIwKNX+EvH6/m9++uRDm0s2Nw5hjAa4VFofOzxG1hfN+by0I3yGtP6M9Tn64J7Qw5Zmi3Ku8ZnAgQ8FokfnVdZfH2la/6At5kZKmehSF8LKgyEHsSQKJdSsnsArNurNRq9IFERMYBjwBZwNOq+lBK3zAQgIJnYNZ9oAEYdqELJDGCCLgujMhNkax1YqLZe7Ay9Ljc7wb7g9mGLxqRR2VYYrCxR3antKwy9CWlvDJQpfu03MuyHMoanCWcNrQbH6zYHpZH7dANv6Yup7lrdlLp91pP/urHI8cCAxp7EkAiXUrJ7gKzbqzUadSBRESygMeBM4AiYIGIzFDVL5P9XkPueYu8wCZ+1eJpjvOtgIGnwvcfgdx+1c6NbI6X7C+vEjwiB16NiSXYK1XhpWrJzvKFAseHK7Zx2hFVk3xG/rMS4OUbDn0Tn7tmJ7OWV+1uC876qqn7J5GkkpPHD4+5VXFtWXdU49GoAwkwClilqmsAROQVYDyQ1EAy5J638PsreaHlQ7RjP3dU/JARQ27mihhBJLI5PnpgZ1qEpSyxIGJqI8tbIX/RiDyKw7IrBzeIzMnyxi2y3LbD4V1qXdq1rPZNPCfbF+qCEiCnhS/0pSde908i3UORWxXXtwVh3VGNQ2MPJL2BjWHPi4DjIk8SkYnARIC+fWu/AtgFgCxuK3dJFovJZduyrVwxunogidYcv/nUwVyS3yfqPuXGxHP+Mb04vHu70I00ch+Ubu1a8vLE40M325VbS0NrX1pkCRd5CxqDwm/OwenA4Tfpmrp/EukeSmYXknVHNQ6NPZBEy7te7V6tqlOAKeDSyNf2TYIJEAvCkiwGd96LFKs5fuGIPF5fWBTa4c6vJLRa2jQOwa5LAXp1bMX20jL8fkV8Ql7H1hR/4/Z+6depDXmd2iDAR18VU+F1U3U+LIc9ByoIeKvfux7WkvOP6c1d3zuyyvtcOCKPfxQWhf59BacKhweCmloEdnM2ydao9yMRkeOB+1T1LO/53QCq+qtY19R1P5Ih97wV6pr6vwuOjpvbKNaUxfBygGkLi9heWsae/eWUVQY4fmBnCjeUsLp4H93btaRdq2x27SunU9sctpeWsWHX/iqD9m1a+GjTKpu9+ytibl/bXHVok03r7Cy2l5ahCtk+N93VTTZyt/0OrVqgAt3bteKwVtmUe38He8sq+WJDCVv3HGRwt8MY0TeXWSu2gyrXnjSQoT3a8ZePV7Nmxz4GdmnLD08ZBFRNY5LItNXIcxKd6mqJBk06xNuPpLEHkmzgK2AssAlYAFyhqstiXZMJG1sZY0xj02Q3tlLVShG5BXgXN/332XhBxBhjTPI16kACoKpvAW+lux7GGNNc+dJdAWOMMY2bBRJjjDH1YoHEGGNMvVggMcYYUy+NevpvXYhIMbC+jpd3AXYksTqpkOl1zPT6gdUxGTK9fpD5dcy0+vVT1a7RDjS7QFIfIlIQax51psj0OmZ6/cDqmAyZXj/I/Dpmev3CWdeWMcaYerFAYowxpl4skNTOlHRXIAGZXsdMrx9YHZMh0+sHmV/HTK9fiI2RGGOMqRdrkRhjjKkXCyTGGGPqxQJJgkRknIisFJFVInJXA75vHxGZLSLLRWSZiNzqlXcSkfdF5Gvvd27YNXd79VwpImeFlY8UkSXesUdFJNrGYHWtZ5aIfC4iMzO0fh1F5DURWeH9WR6fSXUUkZ96f79LReRlEWmV7vqJyLMisl1EloaVJa1OItJSRF71yueJSP8k1fG33t/zYhH5p4h0zLQ6hh27Q0RURLqks471pqr2U8MPLkX9amAgkAMsAo5qoPfuCYzwHrfD7b9yFPAb4C6v/C7g197jo7z6tQQGePXO8o7NB47H7ez0NnB2Eut5O/ASMNN7nmn1ex643nucA3TMlDritoxeC7T2nk8F/ivd9QNOBkYAS8PKklYn4CbgSe/xBODVJNXxTCDbe/zrTKyjV94HtwXGeqBLOutY73/DDf2GjfHH+8t7N+z53cDdaarLdOAMYCXQ0yvrCayMVjfvH+rx3jkrwsovB/6SpDrlAR8Ap3EokGRS/drjbtQSUZ4RdcQFko1AJ9zWDjO9m2Ha6wf0p+pNOml1Cp7jPc7GreKW+tYx4tgFwIuZWEfgNeDbwDoOBZK01bE+P9a1lZjgf/SgIq+sQXlN1u8A84DuqroFwPvdzTstVl17e48jy5Phj8DPgUBYWSbVbyBQDPzV6357WkTaZkodVXUT8DtgA7AF2KOq72VK/SIks06ha1S1EtgDdE5yfa/FfXvPqDqKyHnAJlVdFHEoY+pYGxZIEhOtn7lB502LyGHA68Btqro33qlRyjROeX3rdS6wXVULE70kRj1S+WecjetaeEJVvwPsw3XLxNLQf4a5wHhcV0YvoK2IXJUp9UtQXeqU0vqKyD1AJfBiDe/XoHUUkTbAPcCkaIdjvF/a/hwTYYEkMUW4/sygPGBzQ725iLTABZEXVXWaV7xNRHp6x3sC22uoa5H3OLK8vk4EzhORdcArwGki8vcMql/wPYtUdZ73/DVcYMmUOp4OrFXVYlWtAKYBJ2RQ/cIls06ha0QkG+gA7EpGJUXkGuBc4Er1+nwyqI6DcF8aFnn/b/KAhSLSI4PqWCsWSBKzADhcRAaISA5uQGtGQ7yxNzPjGWC5qj4cdmgGcI33+Brc2EmwfII3k2MAcDgw3+uGKBWR0d5rXh12TZ2p6t2qmqeq/XF/Lh+q6lWZUj+vjluBjSIy1CsaC3yZQXXcAIwWkTbe644FlmdQ/cIls07hr3Ux7t9OMlp444A7gfNUdX9E3dNeR1VdoqrdVLW/9/+mCDehZmum1LHWGnJApjH/AN/DzZhaDdzTgO97Eq6Zuhj4wvv5Hq4P9APga+93p7Br7vHquZKwWTtAPrDUO/YnkjwgB4zh0GB7RtUPOAYo8P4c3wByM6mOwP3ACu+1/4abtZPW+gEv48ZsKnA3u+uSWSegFfAPYBVuRtLAJNVxFW7MIPj/5clMq2PE8XV4g+3pqmN9fyxFijHGmHqxri1jjDH1YoHEGGNMvVggMcYYUy8WSIwxxtSLBRJjjDH1YoHENCvisgDflMTXGyMiJyTr9erw/veJyB01nHO+iBwV9nyyiJye+tqZ5sICiWluOuKypVYjIll1eL0xuFXomex8XFZZAFR1kqrOSl91TFNjgcQ0Nw8Bg0TkC3H7VowRt9/LS8ASABF5Q0QKxe0PMjF4obg9aRaKyCIR+cBLovkj4Kfe6303/I281sLfRORDcft33OCVi/feS739JS7zyseIyCfi9tD4UkSeFBGfd+ybsNe9WESei/xgInKDiCzw6ve6t1L+BOA84LdeHQeJyHMicrF3zVhxiSyXiNs3o6VXvk5E7vc+7xIROSJ5fwWmqclOdwWMaWB3AcNV9RhwN29glFe21jvnWlXdJSKtgQUi8jruS9dTwMmqulZEOnnnPAl8o6q/i/F+3wJGA22Bz0XkX7i04MfgUoh38d7jE+/8UbjWw3rgHeBCXG6wRExT1ae8z/VL3Arqx0RkBi7jwGveMbzfrYDngLGq+pWIvADciMvmDLBDVUd4XYF3ANcnWA/TzFiLxBiXy2ht2POfiMgiYC4uGd7huGDwSfA8VU00Kd50VT2gqjuA2bhAcRLwsqr6VXUb8DFwbFhd1qiqH5da46RafI7hIvKpiCwBrgSG1XD+UFyyyK+858/jNmEKCiYILcTtp2FMVNYiMcallQdCLZTTcRsF7ReRj3C5jIS6peaOvCZWSvB450eWt4px7XPA+aq6SET+Czd+E09N2/CWeb/92L3CxGEtEtPclOK2LI6lA1DiBZEjcC0RgDnAKV5GVkSkU4KvN17c/uudcTf2BcAnwGXi9rnvimsFzPfOHyUuy7QPuAz4zCvfJiJHeuUXxHivdsAWcdsOXJnAZ14B9BeRwd7z/4drHRlTKxZITLOiqjuBf3sD3b+Ncso7QLaILAYewHVvoarFwERgmtft9ap3/pvABdEG2z3zgX95r/OAqm4G/onLQrwI+BD4uboU4uAC1kO4LK9rvXPBje3M9M7fEuPj3YvbPfN9XJAIegX4mTeoPijsz+Ig8APgH153WAB4MsZrGxOTZf81JkVE5D7iD8RHnj8GuENVz01htYxJOmuRGGOMqRdrkRhjjKkXa5EYY4ypFwskxhhj6sUCiTHGmHqxQGKMMaZeLJAYY4ypl/8PheJ18Y+2Kd8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is Black population by total population \"+STATE)\n",
    "print(\"state pop=\",np.sum(tractPop), \"pct Black is \",np.sum(tractBlack)/np.sum(tractPop) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract population\", ylabel=\"tract Black pop\")\n",
    "x = [0,15000]\n",
    "y = [0,15000]\n",
    "plt.plot(tractPop, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for OH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for OH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "892 4625 0.00570439517799993 nonPolygon tract no, pop, area\n",
      "1877 4389 0.0006030292975000102 nonPolygon tract no, pop, area\n",
      "2762 1586 0.002926075715499933 nonPolygon tract no, pop, area\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d57301ff-71ff-413d-afa8-be9a1786a544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 5 ( -81.67383487989999 , 41.48292163644327 ) 0.0003271329910000319\n",
      "124 0 ( -81.82823618543401 , 41.40709590338316 ) 0.0003126030810000199\n",
      "307 0 ( -81.72395529779165 , 41.760512502571146 ) 0.21768287208199968\n",
      "878 54 ( -81.4984252649902 , 41.46137961246711 ) 0.00035936751799996543\n",
      "885 0 ( -81.74041395249337 , 41.4915276927462 ) 0.0002022637595000002\n",
      "886 0 ( -81.5638935967723 , 41.56952511324641 ) 0.00011862860850004824\n",
      "928 23 ( -84.24773730470035 , 39.12476572874138 ) 0.00045441526949999275\n",
      "998 0 ( -80.77030688030814 , 42.101651703794694 ) 0.18273416139700044\n",
      "1334 0 ( -82.52419472587022 , 41.55594362493747 ) 0.0920935166280002\n",
      "1337 0 ( -82.15248201889078 , 41.63486702819321 ) 0.11823042757500145\n",
      "1431 0 ( -83.16373540642299 , 41.8246105716458 ) 0.03870699774700008\n",
      "1613 0 ( -81.25511336594143 , 41.97617459527374 ) 0.21043171148249964\n",
      "2763 0 ( -84.10878061206193 , 39.77955063235859 ) 0.0003971334689999672\n",
      "2862 3 ( -81.85268871436308 , 41.40564241288101 ) 0.0008531598905000339\n",
      "3135 5 ( -82.88686878596852 , 39.99645182979722 ) 0.0012033733169999983\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize low-population tracts.  #SEE FLORIDA CODE FOR PENINSULA COMPRESSION EXAMPLES\n",
    "for m in range(nTracts):\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    \n",
    "    if(notPoly[m] == 0) :\n",
    "        indent = \"indent\"\n",
    "        #x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        #plt.plot(x2,y2,c=\"purple\")\n",
    "    if(tractPop[m] < 100 ):  #and x > -98 to show Gulf Coast only\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" , tractGeom[m].area)\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        plt.text(x+0.03, y+0.03,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "    else:\n",
    "        indent = \"indent\"\n",
    "        # plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "76a12e15-468a-49fe-990a-19fc906d7df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's skip some of these Lakeshore tracts - will exclude from map building\n",
    "isSkippedTract = [0]*nTracts\n",
    "skips = [1431,1334,1337,307,1613,998]\n",
    "for s in range(len(skips)) :\n",
    "    isSkippedTract[skips[s]] = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8979f27-45dd-4894-b876-32480df4a01e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c10aa904-7512-4776-9d01-b0c311351ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract  100\n",
      "working on tract  200\n",
      "working on tract  300\n",
      "working on tract  400\n",
      "working on tract  500\n",
      "working on tract  600\n",
      "working on tract  700\n",
      "working on tract  800\n",
      "working on tract  900\n",
      "working on tract  1000\n",
      "working on tract  1100\n",
      "working on tract  1200\n",
      "working on tract  1300\n",
      "working on tract  1400\n",
      "working on tract  1500\n",
      "working on tract  1600\n",
      "working on tract  1700\n",
      "working on tract  1800\n",
      "working on tract  1900\n",
      "working on tract  2000\n",
      "working on tract  2100\n",
      "working on tract  2200\n",
      "working on tract  2300\n",
      "working on tract  2400\n",
      "working on tract  2500\n",
      "working on tract  2600\n",
      "working on tract  2700\n",
      "working on tract  2800\n",
      "working on tract  2900\n",
      "working on tract  3000\n",
      "working on tract  3100\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'MultiPolygon' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/1341207029.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0morigMAP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMAP\u001b[0m    \u001b[0;31m# let's keep a copy of the original map prior to buffering\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m#below line will FAIL if unionized MAP is a multipolygon.  To be fixed in later sections\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mMAPbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexterior\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufferDistance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msingle_sided\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m#gives a little exterior buffer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;31m# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mbBox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MultiPolygon' object has no attribute 'exterior'"
     ]
    }
   ],
   "source": [
    "# let's clean up tract geometry for good - can do convex hull or buffering to clean\n",
    "# This block takes about 45sec to run for Ohio's 3168 tracts, but 4min for Illinois's 3265 tracts, 8min for Florida 51xx\n",
    "MAP = tractGeom[0]       #method to union all the precincts\n",
    "minTractPop = 10 #arbitrary threshold for ignoring sparsely populated tracts (not used in states with sparse interiors)\n",
    "bufferDistance = 0.02  # used to smooth exterior state boundary\n",
    "for t in range (1,nTracts) :\n",
    "    if (t % 100 == 0):\n",
    "        print(\"working on tract \",t)\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    if isSkippedTract[t] == 0 : #exclude those empty lakeshore tracts\n",
    "        MAP = MAP.union(tractGeom[t])\n",
    "\n",
    "origMAP = MAP    # let's keep a copy of the original map prior to buffering\n",
    "#below line will FAIL if unionized MAP is a multipolygon.  To be fixed in later sections\n",
    "MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "print(\"Map minX,minY, maxX, maxY are\",bBox)\n",
    "print(\"state map area = \",MAP.area)\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x3,y3 = MAPbuffer.exterior.xy\n",
    "plt.plot(x3,y3,c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a1d3977-02ba-4ec3-baf6-5485f2ea118a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OH  is made of  2 polygons\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = 0\n",
    "COLOR = \"BLUE\"\n",
    "for geom in MAP.geoms:\n",
    "    x,y = geom.exterior.xy\n",
    "    if (counter < 2):\n",
    "        plt.plot(x,y,c=COLOR)\n",
    "    counter +=1\n",
    "    COLOR = \"red\"\n",
    "#x2,y2 = MAP.exterior.xy\n",
    "#plt.plot(x2,y2,c=\"purple\")\n",
    "print(STATE,\" is made of \",counter,\"polygons\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "99130811-1d0f-43cd-ac74-a61afedcd8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Map minX,minY, maxX, maxY are (-84.82030499999999, 38.403423, -80.518705, 41.982509)\n",
      "state map area =  11.46323876441624\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fix the above silly polygon by adding a little rectangle to our map\n",
    "\n",
    "lakePoly = Polygon([(-83,41.585),(-82.67,41.585),(-82.67,41.625),(-83,41.62) ])\n",
    "MAP = MAP.union(lakePoly)\n",
    "MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "print(\"Map minX,minY, maxX, maxY are\",bBox)\n",
    "print(\"state map area = \",MAP.area)\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x3,y3 = MAPbuffer.exterior.xy\n",
    "plt.plot(x3,y3,c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f455acc7-7973-4fe2-945b-42b7ca040e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's see if combining state with the buffer creates a single polygon  #copy from Texas code, not needed here\n",
    "bufferedMAP = MAP.union(MAPbuffer)\n",
    "x,y = bufferedMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"blue\")\n",
    "#x2,y2 = MAP.exterior.xy\n",
    "#plt.plot(x2,y2,c=\"purple\")\n",
    "plt.show()   #AH HAH !   This does work!  note that I didn't have to use the geom-in-Multipolygon for buffered map :-)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebcab1ea-2aea-40ff-8826-c2aa38e19f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compare to original unbuffered map\n",
    "#bufferedMAP = MAP.union(MAPbuffer)\n",
    "#x,y = bufferedMAP.exterior.xy\n",
    "#plt.plot(x,y,c=\"blue\")\n",
    "x2,y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"purple\")\n",
    "plt.show()   #AH HAH !   This does work!  note that I didn't have to use the geom-in-Multipolygon for buffered map :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f472dcc-c440-4e52-a752-5c10e99cec42",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to be safe, let's use the buffered map in our work.  save original map for later\n",
    "origMAP = MAP\n",
    "MAP = bufferedMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f72b14e2-8e2a-43bb-b755-30273b8b6f12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OH  area is  11.46323876441624  without buffering.  With buffering, it's  11.792166359835912\n"
     ]
    }
   ],
   "source": [
    "print(STATE,\" area is \",origMAP.area,\" without buffering.  With buffering, it's \",bufferedMAP.area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96462f26-29df-47a2-9094-a750f775633b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "low pop 96 5 -81.67383487989999 41.48292163644327 0.0003271329910000319\n",
      "low pop 124 0 -81.82823618543401 41.40709590338316 0.0003126030810000199\n",
      "low pop 307 0 -81.72395529779165 41.760512502571146 0.21768287208199968\n",
      "low pop 885 0 -81.74041395249337 41.4915276927462 0.0002022637595000002\n",
      "low pop 886 0 -81.5638935967723 41.56952511324641 0.00011862860850004824\n",
      "low pop 998 0 -80.77030688030814 42.101651703794694 0.18273416139700044\n",
      "low pop 1334 0 -82.52419472587022 41.55594362493747 0.0920935166280002\n",
      "low pop 1337 0 -82.15248201889078 41.63486702819321 0.11823042757500145\n",
      "low pop 1431 0 -83.16373540642299 41.8246105716458 0.03870699774700008\n",
      "low pop 1613 0 -81.25511336594143 41.97617459527374 0.21043171148249964\n",
      "low pop 2763 0 -84.10878061206193 39.77955063235859 0.0003971334689999672\n",
      "low pop 2862 3 -81.85268871436308 41.40564241288101 0.0008531598905000339\n",
      "low pop 3135 5 -82.88686878596852 39.99645182979722 0.0012033733169999983\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ignore most tracts with <100 people\n",
    "#Let's visualize our low-population tracts\n",
    "avgArea = 0.\n",
    "countSparseTracts = 0\n",
    "for m in range(nTracts):\n",
    "    if(tractPop[m] < minTractPop):\n",
    "        if type(tractGeom[m]) != type(tractGeom[0]):\n",
    "            for geom in tractGeom[m].geoms:    \n",
    "                xs, ys = geom.exterior.xy\n",
    "                plt.plot(xs,ys)\n",
    "        else :\n",
    "            x,y = tractGeom[m].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        countSparseTracts += 1\n",
    "        print(\"low pop\",m,tractPop[m],tractGeom[m].centroid.x,tractGeom[m].centroid.y, tractGeom[m].area)\n",
    "    else :\n",
    "        avgArea += tractGeom[m].area\n",
    "\n",
    "avgArea = avgArea / (nTracts - countSparseTracts)\n",
    "\n",
    "for m in range(nTracts):\n",
    "    if tractArea[m] < 0.001 * avgArea:   \n",
    "        print(\"low area\",m,tractPop[m], tractArea[m])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "539c1b09-db58-4e2b-b6ee-8c2c5cfe559c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>VTDST20</th>\n",
       "      <th>PRECINCT20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>009</td>\n",
       "      <td>005ACR</td>\n",
       "      <td>ACR</td>\n",
       "      <td>39009005ACR</td>\n",
       "      <td>ATHENS 2-5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-82.09632 39.32211, -82.09627 39.322...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>159</td>\n",
       "      <td>080ABZ</td>\n",
       "      <td>ABZ</td>\n",
       "      <td>39159080ABZ</td>\n",
       "      <td>MARYSVILLE 2</td>\n",
       "      <td>356</td>\n",
       "      <td>204</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-83.36722 40.25455, -83.36717 40.254...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>159</td>\n",
       "      <td>080ACC</td>\n",
       "      <td>ACC</td>\n",
       "      <td>39159080ACC</td>\n",
       "      <td>JEROME 6</td>\n",
       "      <td>414</td>\n",
       "      <td>341</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-83.23164 40.16109, -83.23154 40.161...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>159</td>\n",
       "      <td>080ACA</td>\n",
       "      <td>ACA</td>\n",
       "      <td>39159080ACA</td>\n",
       "      <td>MARYSVILLE 4</td>\n",
       "      <td>395</td>\n",
       "      <td>231</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-83.38459 40.21995, -83.38390 40.220...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>159</td>\n",
       "      <td>080ACB</td>\n",
       "      <td>ACB</td>\n",
       "      <td>39159080ACB</td>\n",
       "      <td>MARYSVILLE 15</td>\n",
       "      <td>444</td>\n",
       "      <td>283</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-83.39649 40.26222, -83.39638 40.262...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 VTDST20 PRECINCT20      GEOID20         NAME20  \\\n",
       "0        39        009  005ACR        ACR  39009005ACR     ATHENS 2-5   \n",
       "1        39        159  080ABZ        ABZ  39159080ABZ   MARYSVILLE 2   \n",
       "2        39        159  080ACC        ACC  39159080ACC       JEROME 6   \n",
       "3        39        159  080ACA        ACA  39159080ACA   MARYSVILLE 4   \n",
       "4        39        159  080ACB        ACB  39159080ACB  MARYSVILLE 15   \n",
       "\n",
       "   G20PRERTRU  G20PREDBID  G20PRELJOR  G20PREGHAW  \\\n",
       "0           6           7           0           0   \n",
       "1         356         204          14           0   \n",
       "2         414         341           6           1   \n",
       "3         395         231           4           3   \n",
       "4         444         283          10           1   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-82.09632 39.32211, -82.09627 39.322...  \n",
       "1  POLYGON ((-83.36722 40.25455, -83.36717 40.254...  \n",
       "2  POLYGON ((-83.23164 40.16109, -83.23154 40.161...  \n",
       "3  POLYGON ((-83.38459 40.21995, -83.38390 40.220...  \n",
       "4  POLYGON ((-83.39649 40.26222, -83.39638 40.262...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/oh_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "71e71811-fe63-4ade-8dfc-42f215e65509",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7cfc5950-089f-4361-9722-c540c64691fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CNTY</th>\n",
       "      <th>COLOR</th>\n",
       "      <th>PREC</th>\n",
       "      <th>PCTKEY</th>\n",
       "      <th>CNTYKEY</th>\n",
       "      <th>G20VR</th>\n",
       "      <th>G20SSVR</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SSCRBUS</th>\n",
       "      <th>G20SSCDTRI</th>\n",
       "      <th>G20SSCLOXF</th>\n",
       "      <th>G20SCCRRIC</th>\n",
       "      <th>G20SCCDFRI</th>\n",
       "      <th>G20SCCRYEA</th>\n",
       "      <th>G20SCCDCLI</th>\n",
       "      <th>G20SCCRNEW</th>\n",
       "      <th>G20SCCDBIR</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113</td>\n",
       "      <td>7</td>\n",
       "      <td>1104</td>\n",
       "      <td>1131104</td>\n",
       "      <td>57</td>\n",
       "      <td>2745</td>\n",
       "      <td>39.5</td>\n",
       "      <td>221</td>\n",
       "      <td>1173</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>195</td>\n",
       "      <td>1157</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1172</td>\n",
       "      <td>214</td>\n",
       "      <td>1169</td>\n",
       "      <td>219</td>\n",
       "      <td>1162</td>\n",
       "      <td>POLYGON ((-96.64136 32.73404, -96.64136 32.733...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0312</td>\n",
       "      <td>2010312</td>\n",
       "      <td>101</td>\n",
       "      <td>3973</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1124</td>\n",
       "      <td>1460</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1190</td>\n",
       "      <td>1290</td>\n",
       "      <td>54</td>\n",
       "      <td>1194</td>\n",
       "      <td>1343</td>\n",
       "      <td>1152</td>\n",
       "      <td>1373</td>\n",
       "      <td>1170</td>\n",
       "      <td>1345</td>\n",
       "      <td>POLYGON ((-95.51892 29.74333, -95.51883 29.743...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351</td>\n",
       "      <td>4</td>\n",
       "      <td>0003</td>\n",
       "      <td>3510003</td>\n",
       "      <td>176</td>\n",
       "      <td>626</td>\n",
       "      <td>1.1</td>\n",
       "      <td>412</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>387</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>390</td>\n",
       "      <td>29</td>\n",
       "      <td>382</td>\n",
       "      <td>32</td>\n",
       "      <td>386</td>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON ((-93.66637 31.08457, -93.66630 31.084...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>1810304</td>\n",
       "      <td>91</td>\n",
       "      <td>3058</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1290</td>\n",
       "      <td>676</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>1331</td>\n",
       "      <td>597</td>\n",
       "      <td>39</td>\n",
       "      <td>1335</td>\n",
       "      <td>632</td>\n",
       "      <td>1339</td>\n",
       "      <td>617</td>\n",
       "      <td>1347</td>\n",
       "      <td>619</td>\n",
       "      <td>POLYGON ((-96.62623 33.65217, -96.62603 33.651...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>2</td>\n",
       "      <td>0877</td>\n",
       "      <td>2010877</td>\n",
       "      <td>101</td>\n",
       "      <td>5743</td>\n",
       "      <td>27.1</td>\n",
       "      <td>1352</td>\n",
       "      <td>2554</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>1316</td>\n",
       "      <td>2466</td>\n",
       "      <td>105</td>\n",
       "      <td>1359</td>\n",
       "      <td>2516</td>\n",
       "      <td>1379</td>\n",
       "      <td>2495</td>\n",
       "      <td>1382</td>\n",
       "      <td>2490</td>\n",
       "      <td>POLYGON ((-95.75613 29.86868, -95.75604 29.868...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CNTY  COLOR  PREC   PCTKEY  CNTYKEY  G20VR  G20SSVR  G20PRERTRU  \\\n",
       "0   113      7  1104  1131104       57   2745     39.5         221   \n",
       "1   201      2  0312  2010312      101   3973     11.3        1124   \n",
       "2   351      4  0003  3510003      176    626      1.1         412   \n",
       "3   181      4  0304  1810304       91   3058      4.5        1290   \n",
       "4   201      2  0877  2010877      101   5743     27.1        1352   \n",
       "\n",
       "   G20PREDBID  G20PRELJOR  ...  G20SSCRBUS  G20SSCDTRI  G20SSCLOXF  \\\n",
       "0        1173           7  ...         195        1157          32   \n",
       "1        1460          21  ...        1190        1290          54   \n",
       "2          28           0  ...         387          28           5   \n",
       "3         676          29  ...        1331         597          39   \n",
       "4        2554          43  ...        1316        2466         105   \n",
       "\n",
       "   G20SCCRRIC  G20SCCDFRI  G20SCCRYEA  G20SCCDCLI  G20SCCRNEW  G20SCCDBIR  \\\n",
       "0         216        1172         214        1169         219        1162   \n",
       "1        1194        1343        1152        1373        1170        1345   \n",
       "2         390          29         382          32         386          31   \n",
       "3        1335         632        1339         617        1347         619   \n",
       "4        1359        2516        1379        2495        1382        2490   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-96.64136 32.73404, -96.64136 32.733...  \n",
       "1  POLYGON ((-95.51892 29.74333, -95.51883 29.743...  \n",
       "2  POLYGON ((-93.66637 31.08457, -93.66630 31.084...  \n",
       "3  POLYGON ((-96.62623 33.65217, -96.62603 33.651...  \n",
       "4  POLYGON ((-95.75613 29.86868, -95.75604 29.868...  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Oh no!  VTDdbf for Texas is in alt coordinate system.  Let's convert it\n",
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)\n",
    "VTDdbf.head()   #did that work ??  Holy cow, it did !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "022a0cd3-0cf8-41ef-aa1d-560064afd963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 0.5407669764770272 5833999 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  7, 204, 341, ..., 296, 201, 215])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump.to_numpy()  #these two lines avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.46153846153846156 13\n",
      "100 0.7811579980372915 1019\n",
      "200 0.4866581956797967 787\n",
      "300 0.24539877300613497 163\n",
      "400 0.8549540347293156 979\n",
      "500 0.7272727272727273 462\n",
      "600 0.4948453608247423 873\n",
      "700 0.5201612903225806 248\n",
      "800 0.7922705314009661 828\n",
      "900 0.6556701030927835 485\n",
      "1000 0.05847953216374269 513\n",
      "1100 0.4766990291262136 1030\n",
      "1200 0.21052631578947367 418\n",
      "1300 0.7208121827411168 985\n",
      "1400 0.33419689119170987 386\n",
      "1500 0.22015503875968992 645\n",
      "1600 0.5294695481335953 1018\n",
      "1700 0.36397984886649876 794\n",
      "1800 0.636734693877551 735\n",
      "1900 0.5689828801611279 993\n",
      "2000 0.7350199733688415 751\n",
      "2100 0.5049180327868853 610\n",
      "2200 0.8631284916201117 716\n",
      "2300 0.7083333333333334 144\n",
      "2400 0.5334900117508813 851\n",
      "2500 0.7065217391304348 368\n",
      "2600 0.6688963210702341 299\n",
      "2700 0.8282374100719424 1112\n",
      "2800 0.7120141342756183 566\n",
      "2900 0.48854961832061067 524\n",
      "3000 0.3931888544891641 646\n",
      "3100 0.36129032258064514 620\n",
      "3200 0.5705645161290323 992\n",
      "3300 0.1597222222222222 576\n",
      "3400 0.7357630979498861 439\n",
      "3500 0.6796657381615598 359\n",
      "3600 0.5733333333333334 450\n",
      "3700 0.37551020408163266 735\n",
      "3800 0.22569444444444445 288\n",
      "3900 0.20665083135391923 842\n",
      "4000 0.03937007874015748 254\n",
      "4100 0.562 1000\n",
      "4200 0.5447916666666667 960\n",
      "4300 0.34776119402985073 670\n",
      "4400 0.6369485294117647 1088\n",
      "4500 0.5215782983970407 811\n",
      "4600 0.6427350427350428 585\n",
      "4700 0.6221374045801527 524\n",
      "4800 0.5081967213114754 793\n",
      "4900 0.13636363636363635 924\n",
      "5000 0.5202369200394866 1013\n",
      "5100 0.41761363636363635 704\n",
      "5200 0.5534924845269673 1131\n",
      "5300 0.546137339055794 932\n",
      "5400 0.4789473684210526 380\n",
      "5500 0.6520833333333333 480\n",
      "5600 0.6106612685560054 1482\n",
      "5700 0.8207885304659498 558\n",
      "5800 0.7358490566037735 795\n",
      "5900 0.7851239669421488 484\n",
      "6000 0.20052770448548812 379\n",
      "6100 0.5792079207920792 808\n",
      "6200 0.3378995433789954 657\n",
      "6300 0.6660808435852372 569\n",
      "6400 0.6738609112709832 417\n",
      "6500 0.43388429752066116 242\n",
      "6600 0.32398316970546986 713\n",
      "6700 0.09850374064837905 802\n",
      "6800 0.36101973684210525 1216\n",
      "6900 0.24847746650426308 821\n",
      "7000 0.3160250671441361 1117\n",
      "7100 0.56998556998557 693\n",
      "7200 0.35327868852459016 1220\n",
      "7300 0.07201309328968904 611\n",
      "7400 0.6833333333333333 420\n",
      "7500 0.8390977443609022 665\n",
      "7600 0.41793313069908816 658\n",
      "7700 0.7583815028901734 865\n",
      "7800 0.17824074074074073 432\n",
      "7900 0.7426636568848759 443\n",
      "8000 0.7367303609341825 471\n",
      "8100 0.7268656716417911 670\n",
      "8200 0.7226277372262774 548\n",
      "8300 0.5916870415647921 409\n",
      "8400 0.33170731707317075 205\n",
      "8500 0.6123693379790941 1148\n",
      "8600 0.6854545454545454 550\n",
      "8700 0.65807962529274 854\n",
      "8800 0.6643109540636042 566\n",
      "8900 0.7151515151515152 495\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now, let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "\n",
    "plt.scatter(vtdCPx, vtdCPy, marker='.',c=plotcolor)\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# See Florida code for fusing peninsula precincts into a superprecinct\n",
    "# not needed for Ohio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  11799448 5833999 0.5407669764770272\n",
      "for comparison, uselectionatlas has Trump + Biden = 5833999 0.5407669764770272\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"for comparison, uselectionatlas has Trump + Biden =\",3154834+2679165,3154834./(3154834+2679165) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a14f36e-b6bc-4c1f-a234-ce10ccbcf832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of GOP lean by precinct voting population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(vtdPop, vtdGOP, marker='.')\n",
    "ax.set(xlabel=\"precinct voting pop\", ylabel=\"pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "5ea2070f-0bcc-45b0-b556-bcddfea7ef01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's clean up the holes in the map  #SKIP for most states\n",
    "#now examine the \"original map\" prior to the convex hull flesh-out\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.scatter(x,y)\n",
    "Tampa = Polygon([(-82.3,26.5),(-81.8,26.5),(-82.3,28.2),(-83,28.2) ])\n",
    "x2,y2 = Tampa.exterior.xy\n",
    "plt.scatter(x2,y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b40b639a-a7f7-4362-b699-cf118a446ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "convexMAP = MAP  #SKIP  for most states\n",
    "MAP = origMAP.union(Tampa)\n",
    "x,y = MAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "93e74ac1-12da-4b3e-8986-2174821d2e40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'>\n",
      "892\n",
      "1877\n",
      "2762\n"
     ]
    }
   ],
   "source": [
    "#finding the TRACT multipolygons to clean them up (like we did for Ohio)\n",
    "print(type(tractGeom[36]) )\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[0]):\n",
    "        notPoly[m] = 1\n",
    "        print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9235c8a4-3aac-4448-834d-ddac4956554e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's try to clean up these multiPolygons   \n",
    "cleanedPoly = [Polygon([(0,0),(0,1),(1,0)])]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if notPoly[t] == 1 :  \n",
    "        x = tractGeom[t].centroid.x\n",
    "        y = tractGeom[t].centroid.y\n",
    "        plt.text(x+0.03, y+0.03,t, fontsize=9)\n",
    "        for geom in tractGeom[t].geoms :\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        cleanedPoly[t] = tractGeom[t].convex_hull\n",
    "    else :\n",
    "        cleanedPoly[t] = tractGeom[t]   #no cleaning needed for Polygons\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1ff818f2-058e-436b-879b-a4691f56f7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#OK, for Ohio, these look reasonable.  Overwrite current tract geometries with these\n",
    "for t in range(nTracts):\n",
    "    if cleanedPoly[t] != tractGeom[t] :\n",
    "        tractGeom[t] = cleanedPoly[t]\n",
    "        notPoly[t] == 0    #no longer multiPolygons\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6cfaa779-a487-44df-a6da-202e6a4160ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "892 -82.66990191014968 41.50421483532522\n",
      "1877 -84.50384823644136 39.208499722844635\n",
      "2762 -84.05673467656308 39.813410750561815\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):\n",
    "    if notPoly[t] == 1:\n",
    "        print (t, tractGeom[t].centroid.x, tractGeom[t].centroid.y)        \n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c019d57-9675-46b5-b812-b058edb03c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#arrggh, I know these are polygons because of their plotting. They should have notPoly = 0.  FIX manual \n",
    "for t in range(nTracts):\n",
    "    if notPoly[t] ==1:\n",
    "        notPoly[t] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6794688c-62b7-478f-866c-4fe85bef5ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'>\n",
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UeHzcEGvn49eNAGPRvx5vfsFmMz03rD/0N7INhKoQeXkvKj7bQdX32dQuyyd4XBJBQ2IRB8ijbvTrKyEWYm4bROWXO6mZtYfTekdy9ik9cBfXs25TMSuq61nt9/NlQSUfFlQiFm4nw2xiWEwIo3vGMGpYEqEhndkegfjyyy9Zu3YtAHdPvwHnsPimdd5/Z1Dr9vHaiB+5ZWIGdsuhVaUGQlUdjBm9nOLihcTHn3bYx2tEqzBcqGVdVPI2lOFLh0ljh+DLrqZ+TTG6y39Ix8vMeJDKyl+bHu9fgxHs7HX4gw5Auw29ME6ZK4E8KeUZQojzgYeAnsAwKWXACichxG6gBtAAf1sJ/b9Hgja9b9z56FLoewGc90qL9cm9+rbaR2vMfR86uGmZEAL7gAHYBwwg+pab2TU0A6FIrP82qlhDizYDMLJ+Bw+kGz+oUWHN6Ynv3NesqJe1egUAtvA7GbXsL9g8lQB4zHbqEicTFZSAIv3sjbdS+GwlpsExTHL8wDzlNO6rfZIQhzG7ybDpPJ2qMHTkMlx+N1ZzKJqU+CWcWRjCvH4VTFovyfz1F0pefJGK198AX+BL4EDoLhc5112HVl6BNysLvyLwO52EJTXIwCoKQlWJvu1WIi8biXtrOTXz91L51S6qf9qDvX80th4RmGMcqKFW0CVatRfXljJqFuaCApGX9kQNMhNxSQ9qFuyleu5eXJvKEGaFXsEWeoWHcGmFBy3IRN7kRBZvLOKX/CrezSvn3bxymLOV5yd2Z/qp3dr9ugLxewzGDho0qMnQWxKcLdZZXMXs1Lvz3PxdjEyPYsxB5IDbi9UaTXLyzCNyrEa0ai8Ac+b/E4AiUx3rva9w3+gE6tcUH/JlWFBQV4KCuh7RMbaHQ5nR3wZsAUIaHm8EzgVease+E6WUpQff7PdFYex44ooWGg82fNzK0IfGxHL1My/x+u03AOCMjMJpC4JNu9HDwto+sGIGqxW+vhXWfQCaYUAdJgu3pjYLK2maxvPPP09Nj0GYqsqxF+wGQLUYJxizr65pW6vPRUL+bOxe44vdZzNs+Y/EavZxDa9yjXwVZ2Q6sf1mUlSkUVu7FUWx4jA7cOxTPl7vq6fGW01JqAAkO8aNRwYILO/dUs53z69H9+uoJgUUw0MthOGa0Tw+VPM59Ct6AWEx83NPQ0shqVqlX3Zz8VL9l88TVD8bu2LGllqJxwJ1+SnULetF3bKCVs8LYOkSQtjlvRAxFqSU+EtdqCFWws/phnT7kZrEvbMSz/YKAExencGDE8krd/FrYfNlvFUIMtKPQMplg6H/PXXDSklJ4YEHHsBkMqHspzC586ZcfvglhzcyoxmRfvTdOgC+wjp0tx+14QpMWBRUZ/vlJnoMHkP2zmUMiYtmYHo0WqXxO/mthFXaZeiFEEnA6cBjwJ0AUsotDeuO2uCOJysKVxBpiyQ9rOMl6XGp3aHR0J//Vqv1xbuz+OrJR5seT7n2ZrTaWvzfz0PdsZPaJUuMUlcMH7fUdZASzW9C6PWw/iPQNUgeBnH9mqr9li1bRl5eHi6Xi7KyMhAKNRFB/JoukAKKHHOxuL9hfhKoukK16Y8Udcvknb/f0TSWWgcI2fIH6gzrR5fUG+iSegO1tduQsnX6piKMffLHZZIfG0mfkO5U1ZWxQmYz7U9P89Mr/8MZkciK76tQTMmkxtiJqPURYxZUa5J6XWIBQh1mpCOa6sgMlu6jG19nanlVYKuYDUu+B6khbKHYnLHYMrrhrZnL3m0ehDIc0icibRJdlbiD3JTWbCX32deJrUqkW9gI9qIThCAEgZ2WQdFCdD7xufno7z8CkCAUbu4Sw4WndiMl7cjk1Tdevv/efkkWS2BD2i0mmL+f2fuYjiX7mWX4hU6IbNbAib1rMOboA8dcTNF2/CUu+pePpn/EaKgC/ruexg6zvxXhu/bO6J8B7gWCD7JdICQwWwghgZeklC8H2kgIcT1wPRizgePFhT+sZKHNxANfvMjHA5bxn5xwvmEKACaTieDgYKKjo7nwwgtR1YP4FsfeDTG9IG0cRLa+XNuzYS3VJcVNj7/49z9QdMkUAaZtO9h7zbVtHto06Sa4686A62bNmtVqmV23M6jccAepQSofOz+mygkgGL71RZ7+m44aEYFzwgTiHvo7cxf2wKIK7PZ0Bg18C4slEiGaf7hOZ4B0GMBmsrUSe4oFMoG9mzewbu4sNLsTqaqkRI6kqz+DEKvx47MrAr+U+IRAWhQcXp2QyRO4enQiW0ryqV69moFjJuBM7YLicGB+rS8iOhNuXGakZTbMHGtKS3j91qvwaw3expxmr6JbsVBqiaLYEs2m8B5UUtNirGYgBEEwghokZfv5UG8/pzcXDDs638/fwpzJ9cuLvFK0mCl9b+OVZ65kQT/BGzPeo390/4Db71y5Cl0zkTG033Ht7PW+bTEAZ4qhxLoMp4Re54PoA+8XcWF3vHmG9jwCaJrbSIRZxdrl6GVjHUkOauiFEGcAxVLKVUKICR14jtFSynwhRAzwkxBiq5Ty5/03ajgBvAyGqFkHnueIUGDeBXSnPiaNU3KS+WafdU6nk4qKCioqKnC73QQdTBcmOBaGXNXm6oHTziS+ayYSacwiBQihIIuKCLMHoZhM0LhcURr8GgIUBVtG20Jh06ZN48cff2xz/a7yXS2+4OcXTaH4gasZc3Ef1IYZypQpWQd+bR2g3i+p7dEce9hOCdspYYZnCMndUkEIbA3+G+nX8eysJHT8OJwj4hkOcM4FLQ9odRoO7s+vo8n/ISVf/7AXv+Zgi7M7OZG98aHi1aFGWqg2h3AgrKrCyGAH31bWBlz/l882sGdVCTde3hfnEVKa/C356IdtfQ6AKaddznVATJVgw8gNbRr6r54wqqWn3vQifQ8hBfZIIzQNqaq4Pc0Nv/dU1pHJgQ21JSkYS1JH5rcnFu2Z0Y8GZgghpgM2IEQI8a6U8tL2PIGUMr/htlgI8QUwDGhl6E8ULt89Fyk/o1D2abF85syZ9OnTh6VLlzJ79uyDz+bbgclsJqlXn9Yrehxe5H3EiBH072/88Ox2O3W19Tzx5P81rc935NPPNwJ7fjRjxKkUxZmpXpJH+rBIrDYP6+f8yOaf59F7wimMOPdCgsKOjIsiKT0t4PJQ6UB6NMPgyQbhHikxJzmxJDkD7gNAj9MhbxUUrqfhLAkIpnRXeWeNg1+jRlIt7ETIOswmcJpNVDd4fuKDLRTUGH7WYJuJR8/uw+uLs1mXW9WmkQc41WfBsa6K1+5eTPI5qZwzJf2w3ZdN6ZWHdZRjS8UjNxH+4PPc/vQibBFtyzhnjDiX7HXl9J3fG+Y3LLzsS+g68ZiMsxHn9jUIBDmOSorVIFz+GpQNvcgc8MdjOo7jxSHJFDfM6O+WUp6xz7IFDctaZd0IIYIARUpZ03D/J+BhKWXb002Or0zx/PnzWbjQ8KsLIVAUpcWtpmn4/X7++te/YjIdmezUp59+murqahRFQW9IVzxt1CkMnzqmQ8fbu2k9lUWFZA4YSc3XObh2VSJ9Gnu1evLsDrpeEsHgHn2oX72G8jfeoGDiufz80VO0pamrqCZMFjOqyUxK3wGccdu9HX2ph/Y6yusprnE3tXdrvE2LCsK2+VPIXQF9zoPUkQH37/Ln71otUwT0SgjBaTUxNiOacwYmkhBmuI50XTJ3azErd5eTFhXE9xsL+d8lA9k2+2uWvP8u0+64h6CQHnz9n7VNx/u5r5UrxqUzuWcMVlP7T/6aLvk1u5yF20v4cWMBu8vqiWy4QiirM05A6dFB/HTH+FbSyP4qD9491eSbKtiyaxvx8fF4vV569OhxTHLWO8RDzTNnl7Qw1vw+K/865Zg9/VdPPsrOFctbLBt5/iWMmnnJMRvD0eaoyBQLIc4BnsVwAnwnhFgrpTxVCJEAvCqlnI7hnv2iYcZjAt4/mJE/3gwaNAhVVdE0DV3XkVKi63qL++Hh4UfMyOu6TnV1ddP9Rso25cPUjh3zx389Tf+ICZTMXguAY0gs25YXkmoKInVsCqExYcx+6X1qNm/CnVVCjed7QNJlyAR2r1zQ4lhCKHQfNRZ7cAh71q8hZ+O6do3h5/IansspbpUnvLqqngdX1zG5yI+9fzQRF2QGLHLyaTpT/9/PuHwtK4Sj3eX8XZvL9LAvjKZAflebhv6GcelkldYxLjMaAVhNCiO7RpIUHjgApyiCKb1imdLLyFy6qMEXP3TGeQydYTSoyF++tWl7m91FRWEVN71XSUhoEUPivuNPhT/T7+w3UHue0er4UkpW51Tyzbp8vttQQEmNB5MiGJQSxu6y+iYD30hWSR0+v4ta9w6Cnb3wVLlY+fFnpFX1oKqimk+sy1psv/aH5dz0SOC4TSAKdmxj8YdvYbY58Hs9eOpqGXfp1QHTfgOhVVeTfd5MHFMmE3/PvQe+spn8N5j7MABvaafS5xj7tvtNnoZqMhtuQSmpyNvb7tf5e6Cz8chxRtd1Hn744RbLTp8ynSEjh3Y4eLXz6bnYio3ZoSnGQcgpKVT8tAdZ4iIr3MaqDa+j+/a22MduCifSEkNe/faAjTTGX3YNlUWFbJr/E4PPOBuAzBFjiOkSOCvp3m17eb+gjIHBzXGMEq+P3e6GvOR5NYT4IOHPwzCFtS46KqnxMPSxOVw8LIVBKWGEOyysXbmF0/95Y9M2mecUoE66FaY83Gr/o0Xlz0v55sUtpOydS2TZBqKuvpLtMy7n5mWTmra5cU8v3jZdz40TuhIdbMXl1VifW8lPm4vIr3JjMSlM6h7Dmf0TGJsZhd2sMOLZjygrNIzf7adkoAhB/0QrWsGpSGm8Z5E7zyGotC+26jTqcPOhbWmLsWVo8fzhkRva/VqeurD1yQjgro++Dbh8X/LuvIuaOXOQDem42575I2dPu63dz93Jkaez8cgJzP45xgDf/fQ9zohgevbsGWCPgxMem4CrrAzFpuIvrqf8fWMW6hJQVOFBUSQ6MPPBZwiPi8NkNlH2r18RCHZUrWJ1+Ry8Xbpg2b276ZgL33mNoWfNRNP8/PrVp0hdp7ainGk33h5wDB5dEmsx883g5qBxjV8jY5GRkXPKJCPAlWcNnJ5W5TKc6R/8msMHv+YAcMXmH1pso3kVVNORE25rD84+PRiy+hrsQwbjKvEja2sYlxnNBxEfcM/Ce+mipLEs4TJqcir5+9ebmvazmhTGdIvirqndmdo7lmBbS42UlbddDLRMVy4pmc36/OZZflm3Lyjr9gVdlj+KSZckj1qEpzgcpcqMxewhbmrbWVqBmHrHA3z242wAbPnZmKvKDrJHM9Xffw9AvRnWjIxkcu9JB9nj5KKxsU63bvcd76EAnYb+hKN///6sW7eOmpqag2/cBq4NRm2aXmeUZweNiCdoeDyW+CAygNU/lDP/zZex2nSkXovfq7BdrqW7GEhe/U6AFka+kcHTz2LYjJkoJpWXb7oSk7ltQacKnx8dmiQSAKyK4L89Engup4xt9Ub2g9uiECh3KbeivtWy5XG9uGj7XFxmEymjSlGDdfT6ChRXhVFPoGsgG271htL08C5HNG9RDQkBsxnXylUASK9xQuoT1Ycfzvu+aTspJYXVbqpdflRF0CXSgekAOjyB3B66HriSWHXaya8O4i/iKYiF1yL+wPqlNxF1w/N8agmlqMsCzLE9SQvvijU4CG99PVLqRCalMOi0GU3HKa6/jLHjIGdPX/YwgKSo7pz+p0NTSPrwz0N44g/vHNI+beEtqKP4P6uJuWXAEcl08WRl42qozgWwZmRg7xsg+eEw0aXOc2ufo8Zbg021sTf3B4JDUjHjY9LmH1D0zIbwl0RKiEkNodvgmCM+jgPRaegPE1eNlw8e/gVXjQ+TVQVFoyDUSCpSVbUpiLt/YHfGjBlkZhrNt/cNwq5bt65pWUdxDImlfmURztEJOIbEYYlvaUpNFsNV8t5fWvpz1zL7gMd98YbL9jtO2+mFs8uMuEPKwra1bRTA1sbrXLi9WcUvUanktJRf6ZsYS9bgC6jZm8jXJkNzf+SKlZy6okvbgz71X0e0bZwwmUh9/TU8O3ei1dYSPGFC4O2EID7UTvxhuKLDw4cDoOuC6uq+hIWtx1HRE4snDtF4IgOWLb2Y+MKe7E1uuAJ0DcW9fSfr675udcz+U04zfNX7EBLi4N5778XhaL9gW+4X/8c/f7qff026+aDbSilx19ZgDz5waut1i5ZQNriIO94qZvJfzmv3WNqi8JGHqV/WHIA1JyXRbc5Ph33c/XlwyYN8vav5vZ4aFdskAhj1UyFoOUZKlQCpSRwhlk5D/1ujZG8Nrhpj5uX3aPjMlURHZ1NVFUNm5nBCQkKaArlSSrxeL2vXrqWoqKjJ0J933nmUlJQ0nRBUVaVXr46nWEbMzCRiZmab63uOGY/V4UDz+RoCzFqTwqSUOpqmUVNaQkxqOgjQ/H50zY/u15rv6zo9Ro1t8znu7hLH50UVXBjXdhZImsOK2sZs+5oxaWSX1OKoy2diUjCJsWPR5Yts2JBOkal5H3Gg7tsA9S2VN6pLitnx61IUkwmzxUrXoSOwOw9t9ugYOhTH0KGHtE9HsFiimDxpV8B1CdAkB52fmMoXT69psV61dGN9ynisg/tTsGUDitSJrq006jEaaOvY7WFKzzOZEkCob3+kXyfnLz9T4SkgLy2X02+7J+B2G/OqWLjaxzBRhBr9C+yKOOwUzEYjn/Dvx6lZsADX6jUH2aNj9I7s3cLQv+dobqZz6jWDW2y7YUEulUWtr1aPNp2G/jCprWguwLAISHC6uGOvUdhzyYbV/OGCc7HZbFitVmw2GyUlJaxdu7ZF1k7v3se2HNxstdF9ZNtG+khwd1ocd6fFdXj/xDA791/YH4/eFwmsX/k4u5cNRfMaVyMDpp7G2tk/kHz+49T89BzKts8Iim2ZtVLqfRD3T8Nx1u3C0iUEa1ooK775nLWzmoON3Xdnc8rF52EyWRBCbfgzAcpvRt4jITOci/82nB9f2UhFQR1FoSovTwuFhopuhk5u2vZ5cWxL9vU6H6pQibIlsXvv1ja3iw2xMUbZwLuWf0E18M5suGMThLa/yEqvr6fgoYdQrDbMiYlNy6XPR/2y5YgDXIEeDpNSJvGvX/8VcN0PL7ZuKh95oNqQo0SnoT9MMofG4po7i+X5qYwNUthODGAYHJc08cknn7Tax+l0kpqaeoxH+tviig3ZTe4fgDPXxpDobZ6dr51tBGb3qHairf0pmb8QwxFqGOfgz9+Ct42TcO0vBbDUcPXEe+PwhY0hq2YdLq2Gb/fswL50WIARCDIzHiQ5ubXPuq1sFYDYzD8y6NRB9BpzdLpRtUVEQhCX/N1w9Tz67odAKOYVJQiXhnecccLt7bQd+ORVW4Jn5XIK15WyoWgBY4fHExSZiWaKxjqi7dl73fLl+IuKQDUhTCr/XJvPwEQPF/YNRXVEEXvHaNw7K5kx9ME2jxH9fHfetVS0XOj3tNquvtpLVYmL0Gg7jpCWhrt2yRKqv/6m1T7+8gq0ykrUqMAqmVJK3A3NRQQCk1XB1Chz7XODu6rVPrUeQXW1gjMkCERLl1RK/h/5auJPmPzAA/tInzS89cERxzaBADoN/WFjsqikdTFhf/8WlOgexE29C+n2gVkhvDKH6+57EK/Xh9vtxuPxYDabycjIOGJ5+B2htsJN6d5aQmPsqCYFoQgURTTdIkA1K5gPQydc6joeV8Ml6j7elX1TNy02O2ob70OBx/jh3dMlDqsi8EZOxZuXgxIegS4lBS4PC6tdTIuNxZuX17CX8UsKm9aXuO5DyGMJ5jgH4Rd0x5dfiyerCt9KD33CR9MnfDQufy3Lurzd4nnT024HIGfv69TUbOJQKc8vY/uKwmNu6Pely+wfSDm9K8XlzVc4p3stvDa0x4F3fLIbViBCBrEk4kLur/wOKo2is/XDzwgcMK6rI+eqq1voOFwB9LwoHxo8Q+Y/52Aendhq3xa49jPyQgFb6wDH3Lc2k7OpHICZ9w0hNq3ZyPpLAndnKnn6aQCSnv1vwPWLPtzOhoV5TY+tDhNXPj4ak0Wl9KHhFAoH16bVEqbF8En+GoLRcAIfx01hU+F1vDc+FlLeAakT7FnP9+POJj7oxJJN6DT0R4CIyy8j/LJLEUKwoaYez8rtAPxg/xN/iz9+P/i2mPvWFnK3Vhx4IwHn3TOYuPSORRO/febfbP9lyQG3ieuWyR8eezrguoviI9iwI48ndhc2L7RFgatxfA4IdZBkt1L9Tcu87+BzbkZ6jEIrX2E9xf9t9s2aleacfbvJyZjd55Od0twIIjHxYiyWKIqKv8OvBZZCMFms+L0eECaQzUHRtMytRNRuorSy4y6rI0H8hbdyxTdV1AkbXiEJ/6icvhPb7wIpl0GImAomBPlYUGsEbmtmzSJk2rRW2+pud9tiPePuhZ//D7R2NOe4bR3UFII9ApCGkQ9qPQN31zUfKySq5cw4/KKLsCQno9fVGfEmTQddQ2o6SpADe//AejyNRn7shRnkbq0ge10pumbErL4sfxiPDOayUogxwzv25YxyfMMANrPANg6n9IPLDyYFzAqqNQmzKxuCBh78NR9DOg39EaJxttPbaefRjES+L6nijn204U8kEjPDyN1awZDpXQiJsiF1o/xf6hJdl1QU1LFpUT41Ze4OG/rygjyiUrrQZ0LLMvfGSeGWJQupLW87b/v8uAiCGyQFLEKgCoEqQEGgCFCFINJsIslmwf3Rh1R+/AnBU6fgGDwYYTYjNUnYjK5IXaKGWJr0cwBD9dmvs2fJOxR3f5+ePf6F3Z6KxRKJxWIYF1V1ovnrAo5tamo+K3OszBpVzgarREiBpkqSfCae3bGeaLMHOLVD79uRIMIcDVRx+/8bj2pWePHmBTiCD+6fHpmahD/ocUqzTNwZ+xwVK6/Hbgnj1p8X4Xh7eMB9GgumGikaYmV4eK7xoMlQt6MoM7yL8XcQ6qua3Tn2/V6TUBScYzsee+o3MRmvWyN7Xakh7icEHtk8My/2QbUjlC85lUVFDzG80IwXScSCIuoV8PUOY9CeX5j11S6SBwxn9IWXYT2ETKajSaehP8IoQnBtUjTXJh1E//Q4EtqgwZ0xNJaI+NZZ7JXF9WxalI+ud7xq2u/xENctk8GnnxVwfVHWTuqrWvs+GwkxqVxwgIydfbH16EHc31r6f4UqcI468NWUS12DVlnLjp3/QggTimJBCDNCKLhcewgJGRBwv56mzfRMh4sKoUZR2Gq1sMxmY6bPRGp0NliK2jXuo8WeTUYsw2RW8PuMtF21HbrptYqC7n4US1pXXswtpdoxgFPjfib98lMwhQcWtvMVFrZ4PNiejz2yIf//hyOviVRVtB6EhWk3nA6Ax+PBs48ipdlsxm63t7V7QMLjHDiCzJRsKeeXr7IQSPTK8lY+/XWOOkZZSoitTyRLGlc6FgQX1Vp5PcRDak0RGdmbyXWXs2bdl4T3zSQhcyQ1bh+pkUGY29HP+GjRaehPQhqlFeR+hrxgZyUle2uoKTFcFn6v1mrf9lJZVEBlUQGn3XInitLa16/rOspx/OIDpKfdRmnpXHTpR0ofuu5D6j4kGsHBvYmKbCO979xXYNVbmPcsJkLXGeVyMyq8J1g1kDqkHX4DdICKyhWsWXN5kwRCM0ZGkNkczvBhP2CxtDwh5m2rBOClWxc2TaZN7TD0/aP7s65kHRb3BjDBm1P8jEy+A3tI2/nvem3Lqx5Pv/sI7uOFhfv0ClaOjJnR/H58dd8hlEgSMv6A3+/n//2//4d7nw5mQgj+9Kc/HZK4m6xwk1dYz8cNYnUSQdmLLxH31780bTPUAWdZwsA3GRToaSpld3kem0J6MXD3XH7I+pLFIy4m6bzNWIKqWfTzpdy+6iH2fnM5uieOy0em8vBZR75Yq710GvrfIDuLa6n1+IlyWpq9EdJwi6iKwFq5g+Da3Vhie9AyEmrct9aWE6SUUlFY37QfwOdPrm7xPHt3VdF77EGCaAfh6YvPxhERQV1VFZc9/p+m5eUlJQi19devsaLU69cJD7IQYmu7+vZwCQ8f3lSUdEj0u8D4O8q46rOR0kty8lXk5r7XZPCFUImMHE9p6Rx8vopWhv7SR0ay8ec8FNX4YFVV0HWQUaBTt7KQqh92gwDd5Sd0aheCG3Tib+xyFWu9y7Ckx+K0OBndbRy2g0hMBI0ZTeKz/0WrrEQoKsGTJ/GjT1CopjPaVE9GXBrYw47I+6GaTFz27/9islhwhtvYvetX+g94hy2bx1Ndbbw+KSX19fWHZOgHWhQqVYGUUF64mf4xu4i+5a+AEfCt3FZGYkyQ8X42/FZq5u2mW5FGvz4lKNbBOIbcQNe4JH78ahHS5EcIiUW3gDA+szrPgSdNXr/OmpwKwoMsZMYe+UDuSWfo584z0p2MXOlmZENQLf+jBIorDb/026e3FP5qJMgUxEdnfERySPJRHGnbnPL0wgOu3207sPRqMnBlDLzz+gtUa20HDncHSG9rLyaLA4+Q1Kf3phYgFl58/Y3mDVQH9qDWRvzDFXu5/3Mj9zguxMbyBya32uZkoVECIdg5Fimb37ukxD/gDO5JaekcFKW17z002s7o8wI3LPfuqUGv8+EYEE392hKqfsimatZKaut/piB0CSMrBhDf53ziuwXuILY/QghCpjTHYaSUXLlgHdAdmxR8EtmN7qUuKovr+ea/RtX3lff3JCg1vr1vA1JKfn7vDawOB+7aWhKHjKT3L/mgCt52eohP2NZk6BvHdCiEqIKQhpNi/KBBpNzTLJwXmxbSIrOnEUe/aKBl0ZzUdIbk/5OXtxiZSldWprFj0Dg+WbmXXgn7HWP7LKSu8aavlBdWfkrxjstBtxMdbGXFX045pPG3h5PO0KuqA02rJzXluhbLn52fxZc7T+dPlS80Lbu2b2uRqPzafL7N+pbC+sLjZugtqoJX0/m/8/rxwvzVZJebGNdVcObAvuhSsnreKAa5lsJ5rxk7NH3xG1w2eWsQy/7LhJnxeEN7N10VzHplIwAZmRo7tqskax3/evS49BqWLWuW0e3fLY19nQdZ+UWYrK1VKwuqjMvwXvEhbC6optbjx2k99l/TP875I0vyljAldQqqUI2KZaGiCAWn2cng2MFM7dJBHel2UlVtXGFt3nJ1i+W1dduprKtiUd4I8lZXYTZLBEZ8yGhCJuidEELP+NYGqm6F4VO39YxESnCtKwE9kmr/CJ6Om83/Un8hu/g21JD7iI4ej657G+IX7fsM4hcYxvzuzyuw+yS/vF/C+vgNVO3YhjXYuApafsVfGHj1BHKmjkMvK2Vgr8EHNM4/PP8KW35urjx9d9FWrA1utT8p/+HTXmFso1na4KCd3/Yj7p4h+ArqUMOsh6WxI1SFhDuG8tB+J4CHZuxXEFmyDd6/gNVeOyM+Dyc2Fu65/CnqdvwVt6/j7tIDcdIZ+uioqVRWraJr17tbLP/yFeMsrJi7o/u2ETR4DH8c+KdW+y8vWM63Wd82NcE+HozvHs2esjq++HUu2eVG0NehFnP+EOPEs2x9L9i7FPrODLi/MNth2X9J7h4OCc0zoVmvQGSik9HX9GfHfUvwVO/vG24/+xp5AM0WBA1duaSU6GopFltrt4DHb3zRNxcYxVIv/5zFnVPalnM4WmwoMa4qdlTsAAzhKk1qaFKj0l3Jt1nfHnVDX1+/J+DyioqlLNg7ine2XAKbAm/TMz6EH25rOwOl/IOWVaqF3s1YzXFUdMkGdw2//HIXJrMHi8WNyRTG+HGr2jXm9/ql84f1Wdh9DQ3PhcJqyw66ac0ZVvGFy/gk+Rr+vqUSMLHYuotu3QJfgQCccs0VLQy9i+YJgluHPpeOpg+j8Xg8aJp2SJo9AKZIO6bIQwvgHhbR3ZmjjOIXdxTnshKXBdwFhrZPxBFqT7k/J52hN2a3rbNJPry2D+d/voPPMrowaXNvrvnTuQF316WRxXA8DX1+9U62FwWzb+PXOTubsyJE48xd11tom5z1v8Xkygpmpqzl/gDHdUZYiU5xNgVhM4YeufTQnJycpllbo55PoB/3zz/PZ7fNGN0atT8Z4+YdsTEcCjf0u4EnVj7B+6e/T7Cl5Szvf2v+x8vrX0aX+lH9Hgwe9D5+fw1mcwRS+vD7GxVNJdsWF8CWHObcOZ4gq4qUoEtDHfG6t1fSVp+JxMdG4y9tKEZo+DzWvPAHxsYu5z3XH1m1shchIaW43UH07GWI8/n9lVRWriQsLKDUeQsmR4aQP74X7mGVVG+pwmzXsKb+H06nE622Dq20BPG3uUzaVgzAuNpFOJ3nH/CYFpuV2979gsId20DArekZVHqhtNZDSkSzUbcGuEI8UbFc9DbV6wvYfVU0wYrgxYbPIj360K5G2stJZ+gFAik1dN1Lo6ScEArDu6aw5+5knvz5Pd4xreQ+9UJDiKzhpNB439+gGthoTI8FmqZTV+FBStA0L+v9MahNlUMGI1I11u6tRBFQ6zVORrJgfYOhlyB1BqjZmH0VeKraEFWSsHVZIcUrtgJhVFTWsLZ4LT0je+KrrsPrqkc1mVBMJkwW6wHFwB566KEOvdZPo181tE6Agdo6OA5uGwCnxdAjqfXWtjL0wZZgJJJaXy0hlgMrMh4OimLBYjH6sQrRfB9Axyh4S4lwcMf2vXxWVMFbfdM4NSqUSKcFj08PeEyhKphjWxqTYQ9+ytwFmVzP20RGjKNv33d45JFHWL7sAvr0nUtwcBl2e8ohjdsRFINjSEuFRtUZhGK18OmEs3gZP/ffGMQWbSdTZr3MnYPv5Ko+V7V5zP37K0dbITr4t2PYG3nzzTfZvXs3t912G2PP7cOPz/8/ykuKaZQwtvboRddLrjziz3vyGXrFjMdTwPwFgZt6VFSbAAsjPhhxwOOYlaOXDbI/897ewvZfmnOz47qXUULLy9PF2VYWP2dUol6rujnFDMorLdP8/tF4p7zh1rLfD/6L5pjExxMHsOHH6+BH+DDzaqJ/aZ3zfvpt99Jj1LiOvag2CKreR1HREVib5FgQVG2839M+m0bvqN5YVAuqUFGFyuayzQBUuauOqqE/EBvzjLPhxvwqMn2Cbl6BqPKywV3F2pxKuscFszGvCrOqtOoYlhTuaBn3UFQiIsZSWbmC4OBzKSsr48Ybb2Tp0qW43b0wh8dRpUF91W4UoVBQkU/et3l0SxlAzxHxKDYTajsKsgDyNy7g/ClLeNo3E1G3Axq8d6HbQ3j2sZtw2Us46+7xZPS4AeUY/sY6TF0Z+OrYtzk9zhhQA489JiaGvLw8fNnVrHrrW7YXLcEvvYTHJ1BRkE95fi7jOg394ZOach12WzISvSHdsOFnIHVAcranloiyYhzO7oiGf8b/BrcDgmBLMN0j2peVcCRwN8ggB0faGD4jHd+bm6iLtjDpil6U1VQSbA9uKvzUdAm+HuyunUiXcKuhGdL4Bdz31hYGkW37RTVtPY3R01GVRezMmEx6YR1BpjCCxyex5Mt3qS4pPvIv9qZfYPuPEJ4KMcdW1XNfhvghxu+n2GRiQ2lrBULguAbkf9xkBFXPfb65neD183Ob7q/OqeSMZxcH3HdkeiQfXN9yIjNwwJu89NJLLFywCmj2x/fo0QPRTTD5k/2yn5zww5Ln2bskBwsm4u4Z0i4/d3ydcZIMFvXMuvhLEkOMNMhGoTi1Dr79x49c9HhPEtNO8IyrnF/g9QBxmqBouGdnwF2mT5/O9OnTKXl5PXH2LoRbYylx76WiwBDdM1uOzlVKuw29EEIFVgJ5UsozhBDnAw8BPYFhUsqATV6FENOA/wAqRtPwxwNtd6xwONLo0uXGNtenAx3IrD5qLP50Bzmby9EUD5UVHua8sRkzgrBiHyO7RgKRAfaKBboGWH5guq9aSW1eDp9u/5QrxlyC/cm32OLsQ7HpH1xQqkPDBNu/xkeSozvSH9g9cFjE9DD+jjPb1pXyv6dhXReNxy42gsgrL12JX/Ozsmglt8y7BV8bHaCOBQvvmcCPGwvJjAtu6HVgLNclPPD5euq8Gn87oxeF3lI+z89leHEVJUXlLPB1o9YTWHumoKCg6f4pp5zCnDlz2Lp1K+ZBgWen26ZeyS/Lz8PrdXBnZQ9CAhh6n9dDfWUlIdEx1GyZw9M5UdjHfcXZwWZs1XXQYOi7Dx3OthW/AFAZHMZZe6NYmXY479AxILaNnhEy8O/irfuXUFfl5Yp/jiLy0p5U7SggdllP+nc9E9EQ64lKOTqqtocyo78N2AI0XqtuBM4FXmprh4aTw3MYwti5wAohxNdSys0dG+7JReMsxxeRijumOfCapEWglRz5KjslKIiQzJ5cnfkgWo2XAv8UuldCd3TcwQVY6+IQukARCqNjz6am7Ng3UDhWeBOME+jyHsaV3FPjn+LehfeSU5PDbYOMJtjVnuo29z/apEYGccP4wCfzv3+lML1vDBcNS2HE4hx2R6dwVZcCQnOD8JZG4G7jBG13+3HZDJOwaNGips5nK+Zvb3KxNHJXVV+qQrLQdeMkWO/QCOTE+u9l5xFmicaTEsNr3X7Cq/h446t7qJI63xb9i9vf+xLVZOKMux9EKyjl5g1Z+M0WpoadWOqPAbEG8/OcKXw76EKGRs0ltVbF5zcRO+FqAl0rN/auePPPS7j5xUmE90/h1P63HpOhtsvQCyGSgNOBx4A7AaSUWxrWHWjXYcBOKWVWw7YfAmcBJ7Whz/3zIuOOWUHQWLAqm5OBpMTaFZxuL0iJr74Sa6UZ0TBt88oa7ErbbpfD5aM33qQ6q5rTMBT4gienEJXeF6EKUAS6X6Ps5Y3Ep7ftvqrz1VFcX2w4xqThINOljkQ2ZYQkByfjMB9aKlxFYR2uGi9SB1eND0eomaZyxcbCRSEIibIRFNrxy+DMCWcz6aZlROkTmBxu5ZE531PiKgFs3Jj3/1AdUOmp7PDxjySerCx8+QVY09NANVFXXEqW6qOkOIW3u2WQ7a5idPwUlAHw1iu/YDUFzhRqNPIANpuNa665hvfee4+8XD/uoLMRplqkPxhf5RAeQuWd7i7Gjo0mNDSUmNjWrfGkLpnZ5W5UoYIGhRVuvoicR5w9Da2hQFFRm+UxzoqP4qz4lnEZn8cNQhw1l8bhkt1lBqneSIrzL6DRkTmg1BHQ0A86NYXVs3LoufUd9PoRKMdQ8Ky9M/pngHuBQz3NJgL7lpfm0oZnRAhxPXA9QEpK+yP8v2W2lv5KSr9JxKSG7GOkoGrWZmp/zmLctsa3LrfVvu6IjcD0ozKuLXt2gwo+n4YZlZq5OdTMbb2d6QA5v5d+fyk7KwP7KRuZkDSBZyc/2+5xleyt4ePHVrRr29AYO5c+PLLdx96fSHsk7vyLyAVyC6GpW1MDjvSnjmuK7b5kTT+9xeMPG25LXzduU2j+EZaffg/d+/VtdQxd1wneYnhfpaLiVxTeWr0YofnpoWn0D72OnVYHXwY111Z44vtySr+2T/ZV32cbRr6BP/b7I/1TxjF74wdU5OQAbU8Udd1HSdEi3r3jKZCiqc+x5vcx9pIrGXpm4PTnY8m25QXUOg35CJNVxd8gcxAeFzhFcuQ53Qi732jgIg5ReO1wOaihF0KcARRLKVcJISYc4vEDfYoBE3yllC8DLwMMGTKk47KJvwGEzUS+Vswml59ep6QSlhHWYn3lV8tBSqIee5SaslJ0XaNwyxaUoBiiU3ui/fAJQdXlgQ9+BLjvrnupWptPRHpcg6a3RGqy+VZr0MzZb9z70mjkL0j9M7nlLnrEhdArIdQIbwvBC+teoMrbtnplILwuYxbojLASFusgd0sFYy/MJDze0SBDbFw5LHx/W9O2HcWqWhmSGs7KPRX8cPtIvJoPs7CwYnclf/96M0+M+S/TMgJnbh1rgkaNpG7pMl6YrjAmdiSW+j64XV7CrQq2/ByC9uwkKDcbgD+Hl5M2pbVx3rcZfVSPPkjNj+bzUZVl9FbQPJsJSRvHjC5R1Hr89E0MZVyvA19V2vtGUbs4D0taCNbUUEImpnCe2pO9V2Wya+VyEjLbfv/mLzDiNAOuB3XTaNYtqeWUhMsItUSRtzUHDt6uthW+wjo8e6oRFhXFrLBb+smJsCCszSejQSFBRFnaN/+NSDBScHuMiMNiN/ZRTAopvdvW2em5dcuhD/wI0J5XNBqYIYSYjuGpCxFCvCulvLQd++ZiSKs0kgTkH/owf18IFboN6M3QRwJL+Cp2O4o1nOjzzmsqidr3J5W3bSnuLTWBdj0i2IMd2MceGdfQaz+GAWG8ZLuEH8NGMenW7zErgk+2fUJdG3rvbdHYgm3YGenkbjVOdLFpIcR2aekdTuweTs7GtrXu20tsqI306CB67iOXXFprnEBiHQmoAVQ5jwe2fv2oW/4LV//5PboVK+RefhX4fKhhYa26Lo0Z2JPQ/XVXGnBGRmF3BpPevTtSSjx1taxrMPR+9xJC1y5heNB4Tr81cIPv/bGmhpD0eOvq3ORefUnu1fqqYl/i4s6msPBL4zixlZzXpfk5k0Iz2vX8+1P5bRaenZVNjyeeGgwte8dzQVw4/+3ZvoBodEowN784qUNjOdYc1NBLKe8Ho5CyYUZ/dzuNPMAKIEMIkQbkARcBB1bcOhlQFOrXluDd22CspWxqjCElYA5DuqpwazozvvmEc3Yvwj9iFLcMvwghFKTPj2624dtfEW+f6ydVFcdVBjjCFsGAqCHsrg1j3d5KtogUNoZkMELTCFNMTYG+jiHJ3WYUDNWUuVoZeimbpZgPC9n6ktTUIH7l006ci05hMoOu0z+qH+789cj6etSICJwTjDoKa/ceOIYORZjNWNK6tHmcmC7p7F67irLcHAJfjLf0qbfJvpKoHaRH90eYO6cSr9fBRaUtlUJD+7XdLWtveT3ZpXVkl9bxwvwdBNfnMcqWz0N/vb/JyEdd1RvhNMP25pqN06ND+a6kCt9+0t1+3c9TK5+iqL6IrcXZFHpykOhMSZ3CTf1voktol8N6nceKDufRCyHOAZ7FqMP/TgixVkp5qhAiASONcrqU0i+EuAWYhZFe+bqU8tAbcR5h6jQNlX26Fu33pax2+/BrzUHDfWKkANjMCsGHIZ8bMiUFz64qpNtv/CAETbdCgC9nM1rJKso2DGT4hldYpeTh+TGJHZ63SY4cyjz3OGoSIuC2tlUsrXaVq54Yg2o6PrPOIHMQHlnHoxeEIAnGy7ucIiW7KzYikawtWkuwJZiVhStbVR8D9I3q2zpQ29SdKgubr5h6wij5dg7uZTZU1eh5KxSoWphPqElQv8KCfciQQ1YzbPGUDftquuTb9fnc9uFaALz+Iy8+5dON168gmr8WtO3HbhpjQ9/dvddeh68hRTL2gQcIPeP0A+3WirPvefCw3qtN+VXUvXc5w+oWGAusoXDW/6DXjIPu63X72fjRInIcLiInj2V4WBAhwWPoGplK5Om9EBbVcBmqAmtac9czKWWLMZ/13BLK65rjCIVE08+8B01r/rxK39iEKdrOv4O3cl+Gkb32XYnhRlT3e/1byrbw7pZ3W433h+wf6B3Z+/dp6KWUC4AFDfe/AL4IsE0++0QJpZTfA98fziCPJE9lF7bsQwr8sPoGwrzVTO71MvXBThAC6095iDYmnKoi+OmOcaRHO1ssl1Iidf2gsx7nsHicw9qWaXWvexP3mp9wX/TTPpc/b6F9CLuBmgnPATDy3Ob0Om+9n1U/NgtceVwavoJC1OTD05PvKE6zkyV5S1iS13bf2Dp/HVfNClz2flXvq7hzyJ0tltVXGm6Igl1eIAyhazg/fhJVMxQvG9z0NHp+9yyHtK++xNa9Y8Vte/QvKIz4ij5vghDGCWitI5cwXecXFgGtM006ynUbd/NNSeUBtzEJeKdvOhMjW17BCIsRFK9bahRPBU+bhmPggFb7v/XWW2RnZzf5463ZmzHV1xpXiQ2534Omn8XEK65rtW97WLCthNE1OU2Fdniq4OPL2FH7AV/vfZ70QUPpNnQkfSe1LDKSUvLaXYv4YGQQ26PCee/ls3hTTWbPWY8RG+6k2Gkn1d4662b27NksXboUVVVRVRWTyUR5XcsiO+/ACKojdX7+x2QmBu9A63crhUtG4y9xUR7WOmhq2e9KcPam+Vy+8lEcvmC8ipvNwffQf7fEpEGvjGDc2ysofX1j0/b+ojVsyf+KzWF2FHMmZ8evJS1oFTrG2+LreQ7mC9/syNt7WJx0lbE7693YFcEtKbE01sUOXGgo+Z3yy1zm+gdRNzWZYCG4Z0avpivQxrS9nUU1vLVsDxX1LYtlirJ28u79twPwpzc/xmLveOpU7H33Ejx1CkIIdKDG7yXUYmscBd1XS3bnKgyaavgSdU2nMKu6haEfK+Zhiep41snh8tSEp8iqzGoWMmsIwjb+u3/x/ZS7y3ll6itNyxq3vWvBXQEDtZ66cjxVryFUB/1soYSvXE7k3/6KLaULuibRNSN7RNck6u4tlD/5b6TL1eo47SU2qoKi0mBiLT3Z4zaKee6KieK1wmL6xwRurdcRCgoKqFoyHzJbN5Q+JyaMNIeVUq+ft/PLyHZ52L/vVcjp09Gqq0AIIi65BFN04DaW+flGeGzUqFEArNtkNEUffs75IBQ2LvipwW3Tcc72PkqcTWM5lwFQ7r8Au8lI1stavYK6yopWhv75G+cDMKHoPyRWlTDZvYoovYK/5RTzWl4p6Z56fh6SiWm/1n5LG05sUQ3LXS43yaKayOgIpg3uxtjgzXxV+gK3rP6OENUPPjCt+38k/us+6pYXcGZ1IjXxFoRZwakq+KXkrP0+16G+CWzwGY58i27jT99ZMfuN71T4nI2U1u/3XjtiCS8qg7AkVEsGz6mJzEozJij93B4mZ22kpej0seGkM/RfFFcCtJjVv5fxPn03rae0LIRvF97H7u09uanHZYzuFkW3mJaz9vlbi3lr2R7U/c78YXHNM/RdAwcD8Pw5U7hgwC2kD4gmsXv7DYMpKqpFM4f923NbS7fj2ZnLZ/+3CiklRdktC3dufG4Cinp8g0TJwckkB7ctD3DD3HiUPBNzu8/lw20fthC1cpgdeLXWEslhcXFIvYrJV1+KumQZ6uIyHAN6Eda9dfZG7SIf5dCmimN7cFgFXcISeHnKE4z7yND0+dVu48x+Y3nLHrR/DVGHCQsLo1fBHroX7iFjwFLMVX24I/lywPi+XpUYhVtvVE1t7Voxx8YSc/vtB32e6B07Ca6pYeC11/Lsq6825Uov//wjLn7kCfZuWofUO+6S2lVstKC84/QBdPnsfQCuynkLp//fTW43e/D+8RTj80mZ+H/0iN7BoOpIBjGc2tDTUWQlOhFMmv0Vr9WOxp3Zj6urTQSPaemjLy0sIV2PYZS/OxfIYDz9wug6vitUmOj99aWQNp662CEEbf0UItIRQuAcmUA34G8HeU1acXMKcd+NLzcZeQBTTCz+3c3b/lryPb7qXPq5PUxbv5uvRn/LvMxmjar1NivrEzydhv5YcFlCJPPKqrksIRIdWPblLLz+CnZm9uBfr74JQJetW4gYsJ4lOwe2MvT+hmDN/r48qyOImQ++xbf/W4em3M7Ccc/QowLWz89ly9ICrn5yDCbzkfGXp/WLojzfyFgRAlJ6RWC2qQyfkd5mDu+JRoVyPSTrlCx9n0f61hNc/CgvvjiHgoIMgp2JeCNbG3rRkOWiqCqy4e3fv+8tgO73s/GJvxFKs6z0oSClZPv2f1BRtRYNO+G2cNZfvp7TPj+NvNo8dtfsYW/NXiJs7W9XdyDsdjuXXHIJ295aTuay0dS5ihlVtJClQ4xg6lfFFVQ0NKRItXVcr3zIypWYfT4+eeQRkqSf2pAQtGpjkvDBg/cQmZTSyhAfCt+uN+ID931maAMpUuOU8y5izXsv4zGbsPj8RFn2yR+XElGVy+Qb/OQ16P4Hh5Tx16AyfsnZykWzUtla8wk/XJjMqyIFyiqp+PFNbDKaO0bdzT333ENZUSnKa3ubUn4B4qIb5LUb2yBmLyQouyGeFX1o8hpjZmaQ0iuCmNQQ7LI/rnXrsCQbExhLejq+/Hrqd5bx5ku3o0k/YU4nsQ88QNhFF9LLbGb43nncPv/2puOlhhwdiYODcdIZ+ie6t5xlzosYyuYlexA7rWzudTOxPxvl7Zf1PY1Te8dRUGWcwRsnhmW1RhlzY9qx1CVl+XVIXRIabSM6xcnCcc80Hb/b4Bh2riqmYFcVyT3aZxjc2yvQqj1YUkIQVhU1xNIi4JTUI4Kkdh7rRMYSvJ4zBzQHlDMyl1NYmMHIvaexqtcX7KrcRdew5jhEc1NzvUkbxL1kKdV7ckDXjRiJ203BX/7adBV08U9X8M2gZh9qe/B6S8jNe4c9dTZqdRWX34UiFL4++2sW5y3mtvm3BdxP6rLD2T6ZmZmk/iWJ5Z9/yMpvPmdS7Rg+/FMfLPtUq+pSBpzRtxer14vXbKYw3rj67FbnoVIMx1P1KiiSstwc4jM7rjP01syBfDMnizpvNTafC1NRKWtm64T3vIqy3GwKQsLYUmmndtl28l9eR4L6PKcnrSZhylOUWuPweApJyarj6zorA91uok2R5EcI4vbJyHa6RvPB1keQ3+2mXJ/BbUpY07qo6/pS+soG1MZCvuBYuPBdqC1q/gF3absZSyDMVpWuAxtjMTaCJ7W8UrYkByNiLWgvGmm35uhoIi6/rGn95JTJrPjDCoQQmISplZLoseKkM/T7E50SglhqBHqkYmLVgDtQdD+v/lLCMysClIM2YGuYnXtcfj569NeA21z2qOEj37mqmN3rSpsN/eL/B+5qSBzcpJoJAtIngC2kRXAHIPS0tKYGzr8Xzgh/mKWlLX3Sa38cjkdEsjT5R7aVruf8b85n9WXNDcsVpbFDlY7S0C6u8smnqGzjOf49UyGq66EpYBYWfcOmTbcbx9YMLf9h7w1rtd2+FZ8ARf9Zja+guS7A1iuSqMvbEL1qA6vDwfhLr2b8pYEv7g/HyDdi9vlwSCv1wkPXoD5ERiRgr7qX7VOvIjh/JGJj/w4fe/2CFXwT80DzggZRsmeS6wkttxDy0cVs1dJ56/3VjLfGkFc/DVjN7I2lzLxhCTU1W7H/PJw306K5snIezuFnkXb/PMzdVQYWRVJZZ+f5Qe+DkLzfczlWfTVjc29gUJ3humvsmqU49jFrPTtQWXWI7FtspppbZ+MdrMH6seCkN/SZw2IRijBmYwJ8Wgabymt5INkwJC2CsQ2PQ+0W0qOM9co+M7iQKJvRPcmsMPWa3liDzHz33DoURdB9xD5NuOc8FHgwE/8K4+/BHB/Uwmj4K91H6uWeMKQOSEJdcy7MNkrZr46+k4pum3BbmyWBw6xhLfZpvKqRukQO7MfC7smcf/8/cEZGNaSnGn/uLVvY+cDd3BF7Cf1n/qV9A3JXw9x/kO9YRx6JhFPOzHAvWb5wxne/3tDpkYZej8PsaCVTve/nBeDeXIbu9qPYTpyf2Lfpo7H4vewq30Zs9DBm6TvAs4PzxXBC8kcRmTUDS2THs7S+CHqt6X6GtSc7PFsI1QzXmT3CS5WzgFhvHANvn8buD97GFrKJJ3s8wJjR48mqzMJcGMOG+Df4T9C3dDv7KYKdVoKH1pA352PcXjNfRi7G6tPxWIzvgUfx8peUZ5ld+jrW1FDQJMKqYo49dhoyAKrJzKk33k5VUQHJvTt+ojyanDjfwuOE1WGmz7iWX+7WuQ9tY7aqhMc5qCisp7q02SB/+MivhETZqK3wcMrVvQw9myYCtzOkxrhEDb+gO8X/MWaySrD5sBoWn7AkDcW8dic+aVTgmpKjGBiWzBPjnwAMo2437acH0mDo577e0MDdZsGUnoY1Jq7FZtb0dAaffmg55Mx7BFa8ylvjruMjYdQDvu08nynVXkb1aTt8prtc5N1xJ7Ubd2DrchrmJGP2H3lFryYjL30+XBs2YIqKwnIcdZye738Og1LCWbungoX1IbxqM65YPWh0tT2I117VQg7gUMmzNhcg7fAYpf7J9TPYtLGYBH80qx2DuOTa0WRWeBktx1PsSeMKz+O8Nc/IU//jsv8AYZjEeYzx3UzO4EFk5b5ASgqQsplnbjJcMlfdrlJnb55gxd/Vshk3wN7ct6mvyyIu/hws5kj8/hoqKpYRFjaEkJB+HX6NbdFnwilH/JhHkpPe0B8uQhFc8lBzEwcpJQve20bxnmqkhNP+mEmXvvt1Sup2Cuxs6FpvC4XpT4LfbbhuAEt8UMDS8d8Vo28jZpTEk1WFYlH5PvmHg+5iDw5h4LQzqSkrRSiCoLBwgqMCpxIeEF2Hb/4EVXkA+HIXk08k04d9QZmIwOzeyiv//h/JhSZWjI1l1NnGbpqmsWTJErxeLzU1NRQXFyOzqxkWfjpu9RXi5WaChs/AMSgWa2oI7m3byT6rWebCmtGN9G++OfTxHgF03ai6XrWnoulHf617Mn40TKjUrywCReDo34H3s4FHRz/KX5f8lTPTz+Tn3J+p8lZRZV7Budl/553gJdwU+RTW2c8zdeA/+Zt/AxudLV2eWu3XjIgaS4ItBs8mlYRpZ5CV+0LTel+4xFwhcJoEjddPrSYDDeTseQW3J5/cvHdaLA8LG8bgQR90+DX+Vjn5DL2UUFMAukbTrLopBa/h1hoCjo4FO4UQTLz0IAGt6U/Axk9hzXts7zqWqpg0bNmLSa3cQ0jY8Vfu9Pv9bN++HU3TEEKgKIqRA7/PfZvNRlJS0mFXndq6hh3S9pOuuqHDz9dEfSmseRfCUvAKMxavj1QKSdyYRdnQCHy2Htg9xnfhoqR1gHEC37RpE/Pm7des3AZfs5LM9F4oiZ9iHjoJe/Rkli/fxN4nn2LfrgGeHQdW8zya+PaRm/ADYVf1puq9rUROTSd4zJEpqjur21mc1c04sZW7y5mbM5f00HR2eKq5ZMUWlu3OZZDLw4QNP/FOWgLjdoVy7oZx9BnSj26pmURvaTZHVf7rMX3lpdvYOUTGRhsFUT+bMJvN/CQlhXWF/Lj7R2IcLYvWpJSsWXs5bo9xdSzUYCzWJDz1xhWGx9PxuorfMuJw8oyPFkOGDJErVwZsWHX4rHgVvrvrwNsoZrhrGwQF6t505Ch1lTLx45blL86ixyirMXPh+HAsy35C0zRuvvlmotsogjkabNq0iU8++eSg21199dW/TUnpsl3w7CDoez4F9XXE7zIKt3+0nsLPCb35Y/47xFVVorsUbMOnUHbOc0z4eEKrw1g0C4pUEAi8QHh5T3Irz+bFyXcbT1OWRNyT5aianwgjxfy4qReW1XoY/Oicpse7/jm9VS3I0eT996bxL39e0+OJhX1IXd0szOcwhXBmcuvOb+9bF1MvPE2PJ0yYwIQJE9p8ntra7fzy62ltrp+9+Q7+fcsthzj63wZCiFVSyiGB1p18M/rybFCteE59Ak2XOJqaJDd86Td+CrvmgaviqBt6v95SStdZlUFBuQLovFak81zfb1m39jRmzZrFpZe2V0fu8HG7jVjDlVdeicPhQG9MXZQSXdfJyclh9uzZeL2tc91/E1Q3GJwNn7CvEMXwus302VpEkrXc0Gm16RAUyeri1YGOglf10tvaG7fuZpdvF6Uxq3E5rmQTfRjgXc8GUwKP3FSOSVo4S/bnygHXBjzOscBuUUmLCmJgchjT+sQdUyMPUNpzOmx4penx/LiN3HvOlRR/YVTFuv21rCz9kYHTziSmRzekXyfXW0L994aR79OnD9u2baO6+sBdvYKCMuiSeiPPFUCW14aCjoJOiRaDPd9E9KBj1+v5ROLkM/TeWrCFMnVhCnvK6sn+1/SW7gez3TD0+xnho8HakrVN92u2/Y0avTlb4Oz4b6DhO22zHdv0LJ/PkHeIiYnBEaALTsdVJ08Q4vrCuHshujsEx4FQcLt0qnPcpKTqYDKD5gPNC8nDGagKrupzFcnByTjNTtx+N//85Z+4NTebPM0afQp2/L3CObvPPXz/0VV8GWykyYbYw/jMvYYlux7hp4E/HZeX7LCYmH/3hOPy3GC4ciJtkXx19leM+XAMAM/WvMMZtjgmnfYHBk47A6EoOEKa68AzieGmLtE8//zzpKamsnXr1oP+FoQQdO16NxdH1XHm6h2MC3fyf92TMQvBOWt20i30t1FQeKQ5+Qy9pxasTu4a372V8h0ASsNbchQbP/sKCtBra5kcOoh/D7iX/klDGb3FyFi4YUw04SWbKVkiWKedChx7Q19TY1xS71i8ASKtfPnNV0gkl1xyCZmZmU3bHY5//rhiD4dJLdMubUBqG6GVKODOwS0F1s7udjabyjbx4dYP+WrXVwCc0mUyT40fAMBzwcZndt/Q+xgQM4AnVjzBlvLj47Y5ESh3lxNhjyDUGsqSmYt46ccneLv2a3bH1ROZnEJQWEuJEF2XvPfDaOJsxQQFTWfZsmX4/X7s7ezM1D/YzugwJ0sqa9lW5+bUqFC8umwlWnaycPIZ+o2fAjAjsSFuX7K95fqaBg2coxi72DmxubouDdg7089rU3XqfTYcZjelQcnU2EZgNkegaRqJiS2DZXur9/Jt1rckBic2Sfs2Syob9+OC4hidOLpD41uyxFCc/GJpy0yY999/n7vvvvuw9GPai/R6qZm/AOl2GTIHuobudhM0+RR+KtZw+3TiQ22M7hZ18IMdBYQQ9Inqw6NjHuXRMY+2ud2/V/y76X6odX/VopMAbx1kLSBq09d013RWvz4Bk6uCMBWwg9uiEdooWbAPt8zezPeL/sLwzDUMFYWUlxuNZto76bEoCm/1TWPm2l1cv2k358WGU+n3Y9mnuOlQqaurY+nSpaxYsQKTydSUmLBvskLv3r055ZSDp1quXHUrVVXfgddBn61vEnv74A6Pqz2cfIa+kf8FjFk000Y1W0VFBbNmzaJLly6EhYWhKAqqqqIoStN9VVWJiYlBbUOuWHE40OvriXvo7xQ+9A+UamOW4TAbvvGoqL3ExpYzYfz6gPvP+HIGfnlg15JAsPyS5YfcfBvgggsu4OOPPzbG6vMRWlpJRbwRDH7vvfewWo1K4qM5o6+ZN4+82+9oveKRR7nl7CebHi6+byJJ4ce2QCYgD7U04qGeJ4np5uTWqfEs2LuAtNA0pqZODbzv75ml/4MF/2wSD6usXo8NSYQ1iJ+tQ5kY0YPY9NbdzC7tk8DsVXkMy+1OTFQCCQlGMWJ6enq7n9ppUnm/fzo3bdrDnLJqBILezo5fHT/11FNNbkuv10tQUBAZGRlNsavs7Gx27drVLkNfWroUsxmw1KM4O65f1F5OPkN/+wbI+eXAHXBsYRAVuF1ZdnY2W7duZevWrQd8mgNlBzgnTcK1fj3hF11EwaP/ICS4H/1GPYtQzGj+Gvbmvk1+fttZL+OTxzM3Zy7fR06CAZcgQhNbSAB/tuMzXlr/EprsmBJhr169eOihh/C53VSXlvDl2/dRptUzaugF5ObmsXv3boAmgx8ITdPaPNG1h/I33gQMbfXahQtbyA3HUEExxqX+oYYLais8FGZVYbapxHcNRSgCk0k5/I5UFqcR/2lgsfVuHsy+lgnJTzEhecLhHfu3jKcaTHb8t/zK3L1elma5+UfN30lyVfDOVa3aWTQxKimcdZNDqF24FkaNI3bAiDa3PRARZhMfDuh68A3bQbeMRYSH56MofjweJzt3DGfGjLubJjzvvPNOUyLDwRgxfA5z5nxO//5jiZ7UsdaIh8LJZ+jDUoy/DhITY+TtTpgwgczMTEP/XNfRNK3p/kcffXTAD1yoCp6cHNZOmYLHHInd78Zma8j/sEShKFagbQuWGZ7J3Jy5JK18E6H54aznWqwPsXRcgXBfzDYbkUnJXPPA+y2Wl5aWUltbS0JCQsD9pJQ88sgjAFxzzTUkJ7cUktPrffjL3JgTnW0aWG9uLgA1P/4IwLwJz6FoHt6zFJLgKySzegsRvnLQRgPtn9Ev/mQ7u1a37KPapV8Up990mNWSfWfC1u/h7u14XpuONXcpDzs+BPnkYbfV+02ja6BaMIWlcMPj34FSz/SUEobbDv4dzbmyoSnNG29Q++L/0XXC0detORCxsVlN902mSpzOMl5//XWsViuKorBr165W3/W2CAsLY+bMYydYfNIZ+sY0wbbcDrW+WoJMRmRe1ySqqaH7jjT2VRUBUhITE0N8gwrg/sdq9Nm1xdLCVSjxkWRHWCHCiql4MtEFnxMfb+i+CJQD+sEbj31uj4FYlGJM3/8Bs2LGpJgwKaYDdnU6EkRFRTU1ewjEvm3bXnvtNVRVpW/fvvh8Pk4//XTKH15lvI5kO4k3B3ahZS5ZDIDuduMrLWPFbe9RpmhMTe7O1eZubFaXsqF6HWGmQ5vSe90aoTF2eoyMR1EFyz7fRW3FEdASMtmM6mYhsF77A3x3F2LFq8Yyc/sCiL9LpNYk9XrG9G7M2fUA1yoVLNvpoPbJlQibimJRQRV4dm3HnzefmFuvwjG4pc86IvnINKs/HFQ1CE2rIyJ8DNXVNVitA/H7weVyoes6MTEx9Op1aEJ2x4qTztD/4x//OOD6erUeh2bMEK91T+bj7P9rJS0aJATvzoqg6vtZWH0+Th3UHyEEBXvysNb4D5pf/nqfQiZoKqovDICImDVs3vI/FNUOUqeufhcHmtFPTplMVmUWHs2DX/cbf9KPx++hTq+je3h3YoNicZiOj+/aZDKRmGgEiqurq6mtrWXt2rWAUYzV1LGjBKb/qjNsWGt1yEay855j957nSbwZEoGq/GRq19xFXt0Oek+YTnB4Gw1dpDSqX92VIBQQKgiFvZsNg7H2+504YwwNodK9tYGPcUgv2gY+F2h+QEJwwxWa5ju5DX1ljlGTUlPE/4bF8XPyTRR99keEDMZfun+Vagz+Qg91S5bgGDyYnlu3IDXjRHEiZHjtHzMbP/44DaQDnHSGvpH9/efFxcVs3ry5ycg7pBEgaTTyPcdMICIhiTXbt1O/9leWOCPY1q0vAHvkQvr8XMG+X0WLpe0ASz9HPe9MCeLhnVtJqTITlryO3VjYuLG5Ys9sbrtYKyMsg38MvQkpNfbR1UQIgdWagKq27Ts/VthsNtxuN3fccQe5ubkUFBSwZ88ewsLCWLZsWdN2xcXFBzxOzt43WjwOTdjLN+uXExl3CWMvGd22ASjZBl8HqoA0/MIen4Inry7A+g5icRopuY/s/7mdeJXnx5TthuuNp4y03HENi2toFurT3VUg/eQp9fx36nDuDLXRWAcuDhDn8eRUU/K8IVGR+NhopEdDahLFaUavN5RDhXr8TxAnAu029EIIFVgJ5EkpzxBCRAAfAV0welZfIKWsCLDfbqAG0AB/WyW6x4pevXqxefPmVq6R8P1mhurWpXzE0qbHaYOGktK7H660TFav/ZW+ZugW5uC7ynqWiPH8+apIMkx2spevQ00Lo3vvPrTF45f+zOPZi6CvH12xUJkwmlhHg9ERCgIFi6Vt10he/gds2/ZgwHVRkZPo3/+VgOuOJTabjcrKSlRVJTU1ldTUVEaMGNG0bv78+QwfPpyhQ1srD362/TM2lG5gdOJohPsUfGHR9O9+FuG2UMzmMCZPaoeaZ2PLt3NfgYyphu6/lKQ9/h3ZJS21/WfceQSkZQf+oVmUVGBcRYQkGaJ1JzMXfwSr3oCMKeD3gN+N7qtn1rcReD1+ev/yKJbafHaccz7XTzVcl9OcgtbfipZ4sqsoeal5hl30zGr8JQ2fuSpAk6hhVuLuGYJQO55S+XvhUGb0twFbgMYoyp+BuVLKx4UQf254fF8b+06UUpZ2fJhHjkbNmIULF7a5jcVi4a6PvgWgcNcO3nvgDr7/7xMttglyOPiu2vDtJtssDEpJQhWCgedNPvggQpNgwMWA0Rn+UOXTfD7jfNqr11MIjC+xy7WHrOxnDANzAiClpKysjEWLFjXFRRrT0EpKjGBo9+7dA/r6H1r2EACf7fjMWFAE52geHh798CEMoMH1ZQsFexgUrIOXxjFdhSWOK1hbfzZJPcLJ3VpBTPIRMMYhCTDunsM/zu8MmXkqO5SV1NTMxadXYAmKxOXKIfyMfPJyE/giejKZ6zdyZ4ORB9iTn8eXX2YbQmYmQ8hs4MCBREY2Xy1V/ZANgBpjA4/EX+LCHBeEKcqG4jAbXdoqPSf9BVUj7TL0Qogk4HTgMaCxRPAsYELD/beABbRt6E8YJk6cyMSJE1st3759O5WVlQCkpaU1LY9JS2f6LXfhaUjvK/P6+NfeUraFJePTdb4Y2I2RYc5WxztaaJqL/LwPAYiNOR1FMeN2F7Bps5Fzvnixiy8+f4iU7n3pMWw8fRNDCba17npztDGZjK/W3Lmtu3QJIbBYLE3b7M/fRv6Nh5c9jCpU7uh2Pk/u+BBVOcRUzVKjBynrPoCynTCrufORbHB35W6twOY0Y7UfmgezsrieZZ8blcyqWUE1K5hMCpqmM+qcbticx/79PlFxu/eyt8H9FhV1Ch5PIV5fKRZzDIoMY+y4d6js27KfQP2GNaz1t6xMry4pZuq0aTjDjWmRN8eo3taKmwPpvsI6fIXN7jhhMyFMJ8bE53jTLvVKIcSnwL+AYODuBtdNpZQybJ9tKqSUrSJjQohsoALj3PqSlPLlNp7jeuB6gJSUlMF79uzpwMs5+uS7vQxathmALhYzzyUlAIbWt8SIAQoBfRJCsVuOTDPwfamp2cKvK84AYNLE7Qihkpv3fgtXzpLFF6HrZt50D+HSEak8enbfIz6OgyGlxOfztZI3bsun7tW8mBUzQgiklIz7aByVnsqm9Tf1v4kbB7RWN2yTjZ/Dp1cFXOXSg9k19DPKasJI7RvZul/AQVg/fy+LPtpBeJwDXZNofp3aimaFxbP+3I+kLu07ZlsZYFLqbN/+MF5fOapiQ1GtKIoNRbFiUoMIDR1EePjwQxr38aCicgWrV18EgBAmZBuFfnppMJ4gWLniLOy4cbFfAFvzE7x9Lc6ISBTVhKIqJPfqR0hdGCkZ/QiOiAJdgm68p2gSc3wQ9l5HV5jwkKnMAbMDgo58RfdhqVcKIc4AiqWUq4QQEzrw/KOllPlCiBjgJyHEVinlz/tv1HACeBkMmeIOPM9Rp7Z2G8XZrzJ+dxgrPEPIr1Q4r3J3wG3/OL4rfz6t442W28JsNtwM0VFTcLsLAIgIH0V09KmUlMwCIFc4WeXpCgiCLMcn3t44awdYU7yGrMosKjwVVLgrqPRUUu4up9Jd2bSs3l/P9LTp/HvcvxFC8P2537Orchd+3Y9E0ieq7ZhHQPqcC7F9jN4D3jrw1Ru33jrsMT3p07XjRtJV40MIuPhvw5vqABZ9tJ31843c/yUf7eTC+w7+Q5ZSUvjESvQ6H7G3DsQU2Wzc3O4CcvPewWyORFWsaLobXfei666mIPz4cWswmU7s7mOhIQPo0+d/1FRvACHw+SqoqFhOQsJF7NrVLA+hRFYjFmTwkPoMABWEsJyBbA8ZR0V1LeaqcvpNnoam+dE1jS2L5iOEwtXPvHScXlkHeCwBfA1XHJd+ZjQgOka0xwqMBmYIIaZjJMaFCCHeBYqEEPFSygIhRDwQMH1CSpnfcFsshPgCGAa0MvTHi73L51F75c0AqBERoCiYoqPp8t67KA3KjR8VlHPb1hzsuHhULuO8VA8TPOH0mDgTAVz5xooWx1QVQa3n6IiiNf6wS0p/oqQ0sBLiUm86tdKoBUgIO76pfTnVOVz+w+VNj+0mO2HWMMIUC+GlWSSljCMqeSJzc+ZSWFfYtF2wJZgBMQMO78mjM42/I0x9jReb09yi2GvshZkMPjuZzx5dg+0AFcMAuttP9U978GRVoZUbrgfd2zKdVtPrAaiuOo2VKwUPPvhgU6Xxxk13UFT0NUKc+C4iRTETG3MasTGtNeK7pF7Pnj3vsnPX33EsVtF+cNHvlFdYb7uOcKopcSYwJ6o7E355CUdiClOub86ictfWUF9VeQxfyWGy/MVmIw8Q2r7CqiPFQQ29lPJ+4H6Ahhn93VLKS4UQTwBXAI833H61/75CiCBAkVLWNNyfChxCRK2DaH7QvEhfPcLiBHPb+haPff4nbmu4H3zqVLw7dlK/ciVaZWWToa9rKAByYecu8T8AkndvotverKaix1BNYJVQbJJouqSwaBa/rvgrAF273kNkxJgj8tJMpmAGDHgLj6ew1bq/fLGBkjonfZNTsVtUgm0mTullCEZJKfG66nHV1FBTWknudj9gx1PvI7lnBF0HxbQ63pHgw21GPOGvw//KjG4zmlu/LXgc1i/iw5wCRtz+FJvKNmFWOm64NuZV8f2GAiwmBatJxWJSyK5zU2uCqTFhDO0SQXTwkUk73bzI6F60fv5eVJOCyaygNNya/JIwAd78WhSragQGfXWYLVZceUXUz9uLLDGj1Xgx79MLWA1pmY6raYahDw6OomvXsBZyElZrLIpiQVWPrarp0SA5dib1pz4GQGS4n4++/DtbSEBaLFx2zgdMKv6Oj+hPfV4O2Vs2k9bTKEjyud2Yj7Gq62HR8wz4cZ8QprvqmD794VzXPw58LIS4BsgBzgcQQiQAr0oppwOxwBcNPkgT8L6U8sfDG/IB0HV4uDlM0DTfikiH6B4QlWncdjsFnEb2za3KKcBsAOL//ncqPvyQ+pUrYZ9A4VWJUfxlRx4SuC4ynFfKKij0xtPFaszC7qlsnjXPiwOPczP9o9djsURRVraAiorlLQx9VfU6XPV7CHJmIhA4HGn4/dVomgcpvei6F7M5Aqs1cFeptk4ab9w0s8nnW5a3l3mvv8BPKypx1VTjrq1BbzhhqdYBmB2TUBSBruvkZa+i3rK8wRdsQ1XsLW6Dg/sQHnawhLfALMpdBMBXu75ibNJY7M6G92rs3axLvpSsbTWcHWrDr/sPq8Drf/N28uOm1ic/gC+BC4ck8++ZR7Yp9KKPdrRaNsapEllQS/F/1zQtW1L0Jbn120gJ6snImBmAl+gb+2NNDaHy+yxqlxagOFr+FBsNfd9+Qxg/fmSLdX5fFSbT7yNtU7HZWnTd2rDhFqqqVxNnG85PbwezwzOBro532FVfwucP3cu0m++k97hJeN2upsDsiUKNXwNPDn5vEaGhg1H2nbiEJsFDVZC7EnYvhsRjm2V+SIZeSrkAI7sGKWUZ0CqXsMFVM73hfhZwBJKU20kACVJv+mQstmCjgGbHT0ZRS5/zYObrAMQPGEXhl7Pp8pkhXyx9RrBImJs/JCEEXewW+gc7uCotjlfKKnjq/F5MseegSy8frm1+vjM9Gsnj/h/x8efTq+fjzJvfg/1zvFauPJeDoSg2Joxfj1G+0H4aA3vZq1eQs3E9XYeMIDw+AbPVhqZpOMPC2bE6DJ/X0PwGga7kUFIyG6s1Fk1zo+tuNM1FY3WuzZbE6FFtp6MeiJenvMzUz6ayoXQDp352KlNTpxIXFEeMIwaH2YEzroBHfnmXDaUbGJ/U8VLDnPJ6JnSP5vUrhuLVdD5fnccDX2xAGIoVOG1HLlZx0wsT8Xk0dL/E79PR/Jpx69PxvrAOc1oIJTuqWFBTh1VY8PqN9/G85JdRxMvoMoiKt+6j3DIS3eVHDbW0CsiWlxsyFmvWXEZ83DkIxYwiLAjFTH7BJzgcR0ao60RCSo3iEkMae4/na3JD7oQSGBhzObt2PwXAj889zfwXn8CrKYTbUo/ncFsgpSRj0YaGRzbelT0QwIjhPxEU1Ky4uUkoZI68GfNhyCV3hN9fZWxkhtE56Pw3Qfdj2bf8XPPBfwc2NxcBZEM3JXODbo30Nxj6/VL/6jUdh6o0VcoWFn3L6op/AdDtLCe1uQPx1UcSkrocAKvVcIVI6cfjKWpxrNDQIVRVrSQq6hQUxUJxsdGzNCnpMnJzja71un54+itVJcVYHUFMvOJaXv1TyxZ21rDbW+hsRfuyUJUgRo9qDp0Yee9e1qy9Ev0wmrDEO+NZe9lanln9DFvKt7CtYhuz98xuWq8KtUllM8EZWCTtYEgp2VxQzeYCWLKrFJtZpU9iCD/dMQ5NSqY9s4i4kCN3mS+EwBLgxOEpreHzwk9x59VTp+t4/EV4gKKEDHJ6bkcxPD4oog57fBUiLAzp07B2C2t1LIe9UXhPUlGxHF36kNKPrvsQwkxE+MhW+/zWEUIlPu6v7N7zNG63k5ni/1iVMxq3C85OnMQc1UdIaCGxIQV4SrLpnRB/8IMeQzIcVnbUexgilzfVzr2Tl4/JEUyqSSFh4lgUKVkcLpjYkLl3rPj9GXqzDbZ+C29MN/IchQI03AphNBbZp1qx+ElD2zznmmsRFjP+QsMo72/oq/067xeU836B0QDhn5VDCOJNVg2yoygW+v1zr7FhwSQ+urU36fHGbEMIM4WFXxAVNbkpIJWYcBFVVStxu3MZPuw74FkASssWkJv7Dt263kdMzPRDns3vS0VBHiFR0YgAM4fuYg2pzt4sK9rMWyPm8XDtejyaldq6HVjMkZhMzoYMDzfV1RuIjp7S4XEAqIrKXUOaG7JXuivJqckhxhFDlN3ITimqLyI+qGM/3BpPc8reZa/9GnCbQw1Kv/zzLvIqXNgsKnazis3ceKsQ5rAwqUcM5v0qLsuK9pLv2oVVDULTmk/UjootbGoM0A67AX59CUfNhzhYYGjkWC8HLmo53oQLSEi44JDG/HugV6+rKCvrxtBtfyNEqWTisO8oUlWirnmAGxL2ybx6LAFSj13WysEQQrBoeE8AdL0nHs907tleyKd5FqChR/Hz7zNrdj5O9/JjPr7fn6EffBVs+RoQDdWR0rh2l0aXIpKHQffpTZurYWH4i4pQHA4Umw1Tzwgsp5+O2C/Q83BGAhtrXPil5IviSuo1naEsBzEJn9/TYtvHlm3lf0PnItEJCxtMRcUyPO78pvXx8edQUjILlyunxX7V1cZJxuHoi93eskz/UNmz3vARv/fAnYTFxqOYTFQW5qPoKj3DDCmC0cXbeNK5l09jEjmTUn75ZVrAYznsXQ5rLPsTZgsjzBbWYlmiMzHwxvswP2c+WcULmJY4kPjYMykpnYUizCiKjfm3JVHhTUGTEo9Px+XTcDf8AUzu2f5gc0mNh39+vxWbWUGXNLle9uXda4YzJqNlCmXOVqMk36M1Z1eomo7b3vDZx/aFsXcZ7sPaYkPZcvdio7Vh/5aG/mRm7NixMPw7473Z+g2xq9+Gl0dDZDeI6WlM2nx1hr7QCYiiWLHbU9jkKm+17gvrr2BV2PLAUi7/56hjNqbfn6Efeo3x104yFi5A6nrAme++XJbQ/KN+qkcKmzbfRWHhl6xa9SIA1/QZymsbLyPcWcW1Yf9kx859lfkEVmvL6j/3lirqIrJZ9tWZ1Ae3vIwrLMwlOnActt106T+I0twc0gcMwet24XW7CAoNQ7qa4wVXzFwLwP2nfYpNL8dVv4eysoVYLJFYrNEoig1VsREV1Q5Zh8Oky5+/A2Bqr1hiQqz0iAthWFoEmbFGZoqUklvn3wqAWvI+0a4/Ywtr6VIaMvhTQkMHHvZYVu0xfqAPTO/JkNQI3lixh19zKigorMOr6QxPi2BIl9aqmf1OmUZJzm7C4xPxbt6Mbf1GNoeUsGGwl/8UVUJIKHx4iTGLN1mapY2dRyfj6VCo99V3qBvZUcPigMypxl/qGJj/mPF+le0yXLCRGZB+YstHzhven1833Ept6ff4fXEsW2ZcGQtdUl1WfUzH0q7K2GPNkCFD5MqVK4/3MA6Iz1dBVfU6hKFghWHMYxoMemMFaKO8qtpKUXLL009TmTwPqWi4QneBYsw8q+oiiYuMZNSoH/i9IKWkXtep9evUaBo+XdI9yIayT6Cg0dD3iAsmr8LV5I5Z+udJJITZ2Vmxk3O+PgeA2OIzeGhPKq4znkTXFIRJRzFJBsc9TUjCOITNhrBaD3rybosr3/iVBdtKWi3vmRDCg6f3ZFTXQ6xq/O8gKN8FmaeB5mkQ92r40/0w+UHocXqHxnok8Ot+Br7TfIJMC03DolgwK2Z0XxEzIoPpFRKOECaM77QZVbVgNofRreu9qGpgt1jNnDmYunXlk7pF9LSEM7Tr6QETJn7vaJqLZZ99wuKf5iB8XkyuWkbOvIxR5194RJ/nsCpjOzHQNR2fRzN6hkuQBBEeNg61g8p4udt1ehYbebXBk5N5LrGe/+Z7wQkDTHD0clCPHmev3sHyqjqGhDio0XRq/RolXj/eAJOJF3qlck5s86y4f1Io4UEWXr1iKDvr3Vz3/DL2lrv4eXsJFw1LYc36WTz+hp9fuitUxUaREtSTr94aiE93NwXI47c+SqGnpbb81+On8OK5lzBt7UKGFuxCNVswWYw/R2g4U6+/BasjqMU+d5ySSZTTyrAuEThtJmqEpNACf+oWj9KRloO3rj70fY4h+/dbyK7KbvE4kXwyHb2R0kdBQT4Ws8BiNa564mLPIjR0QIvt3Vu2kH1Oc2bZc3eo1NsEG8oLYPj1R+dFnMCoqp2Nc3/CXN0s7pvQ/cgX8h2ITkN/AJ774zwA7CEWXNWtm4nEdwvl3Ls71r19s8vDptqvMAWdyphPbuOBhAVs9D7AvMlTifmNSttmuYxYxcrqek6PDsWnS2bvd4naM8hGjtvLiqo6zokNp7rMRdaaEmpK3SgenaWVtVywbhfBsVYodzFrUyEXDUthzLJqKgshvVDn6Ut0zhoexDc/X4suqvl+77fU+cuQ3lrCLryAyo8+bnq+zd3MjFz2KfMGnUm8pxrF48K6Zw+Kzxjr8LPPJ6ZLy4bT/ZPD6J8cdnTfrBMIs2JmwxUb+POiP/Nd1net1m+yzGTokEcB+OyJt0ktjyT8tEp2+v+GEqBoK3vm+S0eW33wT1uMIUtxknLGbfex5JN3yRwxhvDYeLr0O3wX46HQaejbQVr/qKaAbniCEyEEO1cWUV3iOvjObdBnz0629riM8PIteHbmwwwolyE83SOF6TG/TUM/b2gPzlu7k90uD2dGh/HHza2F6bbUuQnxSGavK6Dvh3mU5xuBS6/Tx7Z6D29/uRVzTQ2+AuO9Lao2DHLkpZdR8fFnrExJZ/6gkegWlVr5JnOrroPgK5h1wR6279jGmD1hiJhMEmtLGfjAHQz67G9MXSPhw+/I/58XFKguyiDrS+OrHxrXsZTO3yOP27rxeLaRIFCoqmyyWvADMSF5TdvMuOw8Kr/NwpNcDdlgUlsHRFPfeYeS//6X2HvvwZqZyc9tqJSeTCT16sOFf3/8uD1/5ydwALoNiaF0by0T/9CD3Zf8Adfq1XRbuABzbCyVhfVUFnfc0A+4bAwJT9yMJuC5MxSGP1T1m3TX7EuUxcQXA7tx4dpdLYz8RRvyiBzs5DmvcQKrtgqqrWZqa2voNiQGv1en67YCXBbJpr2VpNlNVNhMpEc7efoCo97OkpRE+opfWZRfyv1C4FRVHtptZuwqWGTzsX12DNuJYRbAKOOyOOvc6Uz9a7Oqp3mPwJcmCYndwbT1FkKTXFgOoktzUvHd3U134zSNuHrj+73ZNZvVay7Dbk9BVR0oEy3k534CgMnU2tA7Bg0k9c03Wi3v5PjRaegPgBACXTNS6yKvvRbXhvWYGtJhVJOCFiDtbn+klGhSM/705lvHFZfQ85prcPvdvGD6DWl2HIQIs4mPB3Sl5+KNTcsia7cz85qXOVcIHr3jPuZnGMa7SNPwrjS08IZh5vLBKQyamkJ4XFDAY9tUhRuSmzNU6kdO5GnLbdRsCTxTknrz56M4gggOKaIcC12z6kg9v7ShxOLAPnddGur1J0LP0qPO38rg8RTwNsc5dAFVISbqK5ZSUbEUVQ1C171I6cNuS0ENMKPv5MSj09AfAIvdhNdtZMMET5pI8KTmhiUVhXn4XB6eu+ZipNRxxJeRPC4Xs82MlBq67qfcr/PPAht+AhuJQ9ZYP0x0TeP7Z5/EZLFy6o23BTRe1aVGRe3+AcpDIdxs4q3eCdy0cRvhlOP2G4ZDkZK/Pf045z3v4IfV1xPkiccWEk6PYXH0nZhESOSBi5o2/7SSL379lFhfJNPcI5gxZhxXXXEV7y7fwwe/5pAa6WB633jO6Ge4YzS3D8ekh5CeauKSXse2aZ94gYKRBXMAqv0aI5ZvpsKnYVMEdlXBIhQezUjkjJiwDr8/JyyKCg/ktVwE/P5qcE8+TnpDX/nNLlybykCC4jARfUM/lIbydotNxV3rawrK7o+u19Fr9HiEEOQVvoFi9hEfdzGKauWn7W/Q3SYZGJXJitLW4lcA5e7WBRVHk/L8XLYtM0TGQqKjMVttTU0c6qurWDvrO9y1NUQlp3LFk88d1nOdGhPD7C4fkJfnJa1LKtZrryFo9GhW1TxDxS5Jcm0BVVEFRKem0n/aQJzO1kZ+Z72brbVupkU6WVe4kHxxPSMarE7tGissBs7oyqUjUrl0RLPuia770XUXqtVJ6vONKWzXsXHSWNw1ZfR65ykcycMO2s91j8tDuU9jRkwYEWYTJV4f35VUke3yHHC//dnx61Lmv/kKiklFSkmpUk3WKCtqhBO7yU6/6H5c1Sdwk5ROOjkSnPSG3rWpDGFSUEMseLKqqPulEFOMHXOMg95jEzGZldZtJyUs/+wDNO92Jl7xOoqqMu/zlUjmkZ5+B2ZzCGsq8qHmO24feCt/+OlPAZ97QNAfqHH7jlmrv8rCgqb7yz79oNX62PRuuGtrCIk+MgU8XdNvo2s6MLZ52ThGMlrTeOSRRwDYuWcPL7zwAmeffTbpXbryxdOrKcqubuEaGziyUUjawb8T6rCqAtUbws9V33MJY5n39hY8Po3VO8qwenUGntGs7XPrj49Tpzi4fGQqF+eXEgTI9y+GhysPOv5ir5HLP6esmnqt2Q3UI+jQXG17NqzDVVNNxvBRbFk0HwtQmlOI3ZnMkvwl/FL4S6eh/40ipUSv86E6DZlpf6mL+g0l2PtGY446vr0g9uWkNvTSrxsNhAG9oUCnsemwOT6I2NsGMezM9ID77loRSsH2Mr566t9M+9MVyDBj1q9rOpghyGz0UL913s1A61z7j3fXs2nnn7g0/HK+uvP8VuuPBkXZOwG48ZX3WPntF+xeu4rM4aPpN+U0rA4HXreb56+5mKSerbs5+f21+P01qKrDCMgdhna8qqo8+OCDTcbeZrPx3nvvMWTIUIqyjR/HvvGPy/reyTsbnjaWuyW3lwcRF/cSf0u6ga//8zRv1b5OpW8c3tSR9FDWMRCox85XnMeNQa/ypOtW3l62h7+/eTvb5j9MkZJAkKuKcFVDVW0oig0RoKl6kdeovN3XyANYD7Hop6ashPC4eKbfchfbvNn4f83mzZu/IdQRxq3zbiW3NveQjtfJiUPdsgIqv96FvW8UtsxwahbmUlNYwp5PNzHiL1fhTDgxWhme3IbeqzXd12uMH7U8Lxp9aQWKrrW1GwBmm2GQslYtxW5vbjw997uv6LcinfAxDlwOUPdzg19XWcUrYaG8F6ljkxvoGvQyL6wtxWay4bQ4mdZlGsGWo9MebvlnRhOQ56+7pKGiF0r2ZLPxzVex2B3QIM0clZTSYj8pJct/OTVgsxOzOZzExEvomn5nq3UHQlVVhITB/nQyCuP5ybaCtfNmYUlKZ3DKFLYubVb8vLz7aSzf0ZuVOyoYMDoRZfEfKFRqScwOYvG2d3l0lQbMB+bT/bwC9vb+gfprr+Zm85c8P/MivP0j+DDNzHfpo7neNRyz7uGxpWeSKPICjs3p7MWgge9y59a9AdfHWA/tZ1NbVkZwVDRVZWUUb91GqGJmzCdjsZvD0bQ6hsQdW23yTg6Mx1NCaekchDAZ8tCKBYc9jeDgnq229RUZ6cGuLeW4NpSCVaGcIrw+N7989AGT77il1T7Hg5Pa0CsOM6YpcXh/ykdpmHWLz0pQgS0hu0ig7WYbfSdmkLMeHKFhlOfnYfdchMv6IdtylpHmsOAPLgUN/jTgZrxqFK9teo382nxeCTP8wl8FOwnTdFymUn5a93zzmFA4L/O8o/J6dyZUklrkoDy0jmq7md6lA3CWb0PVNPz1dfgtFkJ9PmIiW7puvN5iPJ5CgoP7Ehc7A02r+//tnXd4VFXawH9n+mQmvTdSCDWBhN47ggIiNnTtve5aVl1117a7+lnWXdvaFburYBdFUaSjdEKv6Y30On3mfH/ckBCTUAOBcH/PM09uOffe957MvPfc97yFqqrf0OoslJcvorZ2c7vXrKmp4dVXX8XlcjUVBz/wGenpTR+vksxswP5QfitbRZ0lgOdq7yd3hOKeeb1tEuGGkWzYuB4N8MKSep7YMou+Ef/l1bgtLOxzH7PW3wWAMdCNWyNwPvAXAExuF3/+3/tU37KBzftHkLtyJwxMo2/uOkIrAviJofTss4UEP+VNxyuVB3N9/XY8nlqmhAawsKIWf62GFD8Tdp+PML2O7ubDm26klPz444/k5uZSn7OP0px9ZN12NWE0u3Pa3VX0LBtCbNUQ1nly0Bu0RCYHEJV0esZRdBW2bb+bqqpfW2zTaq2MH5fZqu2GzN/ICywhdfJwQhZ7MFXqiNV1JzakOzv2rD5ZIh+WM1rR/+9/TxER+SaZkVPYZCgnxBOIQepxCTd7AvKZxBXtHqszKD9YW00179x9MwEJdSSfDf3Tf6KIn6DxheDlzJcpcbd+1dcKLcuu3YwQApvLzZbyrdzw01VYT2BGvstWVRJWU3HQlh9b7A+65BJqf/gBv24tR/R2uzKyTeh2BxbLUCwWC0lJyrzDsuVDMJnaDzoqKSnB4XCQlpbGtm3bANDr9ej1ego05fTxxlLiKiKnfiseSwA6o4lc/2Yf/GdSpnF16V6WPjyeJ1f8k7P189CuHE99UDxCY2B/WCR//edN5K9JpvCseB7rE8c5k8tY9dLP9N3+NquG+ZNanUo5tei0diCNMKeT6Q3LKdCF8FxFEZNqQxgSUs28TbGk5wQwZtY2jMZI3u9vaOeuDo3b4eCDf/yNPIM/Rr2BA2fRaWIIr6khpWgHowfs58aoaCbuVb5jq7dnARAWb+WSvw09puuqHD8NDVlUVf2KzW1ig30maZYFBGnr2q32dlfSM8rC9ve5JesFhvn5iDIov/fwUT1OltiH5YxV9FJKIiLfBCA9fSHv5LfM3JcQcOjqNUkZg5j1l0dwO+xInw+v14nHsJ2IxASERouUgo3lOynJVwqJjI0dy7LC5sIeXukl7c2JCF09aNwIoTwZ/ERUm9c7XqTPR1hN65wzwmRCer2EXnMNdb/8grFHSiu3S5stB4AVK3axZcsyZs2aRUZGBh5PHW53JWZTt1bnPUBenhJpGRERwdatim+9y+XC5XLRoG3gLe2ixpLzoUAo0u1i7pMeFqULXp+mpXtRLfM++55Loz/jNepYNDaMsG4ROHaM4ynfv3gm5198NzyOx8TfWFCsJ2NKBB+8vpfasH48Mf16etXZyNH/RpSvF35eP9atmMjDPbvR35TEnVkZXLm1ho/65rB14/lctiMDu18U8su1aM4+NiW/bPkQ3O5KEiZDhN3K7sxpGIObzVpVgfC/MYtIKRmMvns8oLh8+rwVSF89eLWsnLuHhupKeo8cR7e0ji1/qHJo/PwS6e7qTeJvKzgX5U27JNyI4bJ/tmpr9xwUMNn401pt8yJqCrAOKOeqy+9sdUxnccYqeiEE/fu9TubWO/mkonXg05SEKYc8XqvT0X3Q70deLfO5x0Sfx0MbP+GatGtaJIoy+HyMtTuo0RSwsnYM0msBJNIdTKJ/yrHe0iHx2Zq/lKb+/fHV1eGtr8NXWwduNxVvKg+9kGtbe3/Y7DkIoaN//4ns2fMl3bsrZewaGhSTh8XSflm7lSuVkni//KJMVt99990IIXC5XDidTlwuF/V2Jw/+UECfUB2Ghv3APCZlSlb3uIDlHjPB5y3n0Z1BPFdbx/JlV3CpczTLQrTErPkK6wgXtvoQnqmuJaSkhn/P78nHSUH0DOrB2God+8xhVHqG06++Dkq+5LypT5Hu+o6/7a2i/+dvkhWlY0rsKM7K702O1oy1Lo/d1kG0WWqlrkSp+Wn0h4SRoG09Ie12N7vMms319O33M/vylLPF9Ahk+u3pGEwTeSl3P0E/bcJR9VKL4wu2QeF2DVL6qCwsUBX9SUYIDYmyH4rvrkJUmRPK6+H3mamFkbCoP1Be8j8Q8Prwu3k8W8O5ZCH+eHKLfx+OI1b0Qil3tA4olFLOEEKEAJ8CiUAOMFtKWdXGcWcDLwBalKLhnZfw4XeEh09m8oRtTPB5KbGV4PV58fg8eKWXpMCkDrlGAMHUVdtZnr0Sk1SCkDy6ep4rLQcgyXE+Eg0XD4rj8w0FRAWeGJcsrdXSogjzwfhcLnx1dfjq69HHtDbD2O25mEyx9OjRiwceeKBpe0PDPgAslvZfUS+//HKWLVtG//79CQsLIzCwtf35/s82s7XUydZSJwE+f2LGvcSHVifFHkj0zyXAFoC91wj+vdoLGsF851qG7N1M1JdbWblwDeUL91DjWM+g62eRtVSDS2dna9hmrv7awxdXRJJu78GOiBCevPdV7oscCY/dx94fInCjI7nEwxvmXO4MuJw9PjduYyDDZ7XtacX398KOb5Xl1POVcpW/Y9LEfay4cSIrYwZgF42T6lHLuMZvIon3NCfA+1NCJJFFSzl4urfv2ImEJyQRGhvP4vfewGg59qC1g5E+iTiWrJvHc00pcXp8SJ+HdQ0uqjxeBgT4EWfUd3iUce4VV+LYuVOpCqfXIfR6hE6P0DUum4xEPvAAfgOOIJGYlLD6VWV5zL2wXKlAx9d/hHt3tWi6srqeHYZpaKNSCa58i1SfhZliAQ91/xOPd+gdHj9HM6K/E9gBBDSuPwAsklI+JYR4oHH9/oMPaHw4vAycBRQAa4UQ30gpT27BxMOg1WhbVDiSUiKRuH1ufNKH1+fFrDMf0xd09qqHALiO5tF/6ra3+fwvlxGnHYtcpjz5fRIi/E2tytOdDDQGA5rQUAht2xXsQE3b7Oz/NrpWGtFojOTmvY4QOkym9qth9ejRgx49eiClJKc2h6zqLLQaLTqNDp3QYdQaefS8FOqdbjbkVeOtcILQ4K/xUYyGi/Wr6N/vSSanDGGJWclhs3zZlXw7Po4xVgtjL5xEZFoxtXXpBIRF8dSUr9m2cD2vVfm474angQosxWPoHZnNylV/JinxVcIjRtF9+kp+qfknPY39eDlbh8O/iCsj/k5A8ngY/mCb93JV9WrSbH/B1JCBZpERUbURba8A+kQHMK5nsw1XXnQ/9l9bTuYF35ze6nzDL7iU/G3NE9nbl7UMzOvWr7ViklJyxdurKaiyY9JpMRm0WPU2Znf7M9Gh3THogxu9RXT8s3YKFTkxXLhc8QzpNWoNRl00Cf0GHPWbwp49e4iKisJsNqPT6fB6PGjbSFZWWudg6BOLmta90WbcvQLBqJTFTPEzsnxo7w5T9tLnw7ZuHab0/phTU5FuD9LtRno8SI8HX0MDDcuXY9+UeYSK3gdaA3hdzUoeYNA1rZr+Vq1EfHsNCazbuorszyPYQQyLnzz1EuUdkaIXQsQB04EngAMGx/OA8Y3L7wFL+J2iB4YCe6WUWY3n+aTxuFNK0QO8lvkaL29qPxr0kl6X8NDwhzrkWjlJRnLWu9gSlg8EMCwxiOIaOzFBJy/nTVnZz7jd1Wh1foQEj0Cvb10x6fdkZT/XaltAQDoazeG/RovyFnH3krsP2ebVK98gt6A7lifeY8joKLb0G8RnnnPRb32chJw/ERKaQUnVDtxBefwYvJYf3/+R+Iq7cPgs3Gh/k6DQGfRI6sXwC3sRU5PH9K+eBmBPUjDhv41ke/kKIiNsxN32Pf9+fxGfb3cyM/Z/zHx9Hf8b5eSrkVpe2fEZPT0WZMaN1JaXYqupwVZbg8Fk4uoePugxh61bJ9CQnY51QypPZSllAp84P43Zg+PRazV420iU9txzzzFjxgwGD252peyW1p+7PvoKZ0M9HpcLt9OJx+XE7XTgcbuJ6t76Tam83sXKvRWkxwUSGWBi4XbFDTXAN4mLDN82tbNYerK2NpI/ffsvvH5T0BpSqHbMJ/fnIAp3bqNb2jOH/Z8d4LvvvmPt2rUttgVn7yVlUDrT77ivxfZ9pQ0t1rXFdrTFdhxTlYFUlu3ooooPxwGTZMCUKYRer1SW21m5k8V5i7kl/RbchUXsmzwZbUDAoU7TjEYLD5eB2w7OenDVgdsB4b1bNX2oewzXxIaRW1VG8d+Cmrafazr14iKOdET/PPAX4GAH70gpZTGAlLJYCNFWOGUstHg7LQCGtXUBIcRNwE0A3bq1P7l3ojgwou8X1o8t5Vuatt+Wfhtf7v2SvNq89g49JFc+PoKqEhs6g4YQkx1fQQ5fZhcxJnopy9efh47xbMgpx42OGf1PXFX7T2+9i/3V+XRL70/KqIHsd9/VtC829nJ69/pHu8dOmqiYaHw+F16vA5/Pic/nxOttwGg8fBTt4789zqe7PgXgwh4XMihyEB6fB4/04PK6+Dn3Z9btX8c9S+9i9eWr+di/jAs++oxv7oFy4NeaYfwcX0i1fgb3azcxrPcqlhSEMKAykfmVUejw8AdTDju+vQfu+ByA9b+s5+GcJC6QS7k2VqCRPnr1zmD4cKVebtCoSKL2LyS3NJSVfWyMu9jNeA244v3532Yfe+c/j9nrwOBz4qfxEBRUTa9ZSg40i18Nbnsd7iQ/HhqZyL+/y2Th/K9w7Y0iOS6KwsJCtFotsbGxTZPRSj+3rour1enwCww6bB8eoKhaUWx/mtiDyX0jeXtFNv+cv51BPSYS4J+Lw1mMy1WGVmPm6a1PUSB9+Dy5dC/ag8bdAARh9j86983x48e3UvRVSSlk79jWqu2Crc3R1wKao8qlBCFINBs71HTja1AeLJqDzFwXf6sEIE7oNoHEOkUCTcBRxqbozcqHQ9f0jDMZiIuOxfv6K1Q8cj0NA+xM7XvP0V3rJHBYRS+EmAGUSinXCyHGH+X52/qPtlm7UEr5BvAGKKUEj/I6x01yoGKXvan/TawuXs2HOz4k1hrLTf1v4rfi3/BIzzGdNyDMTEBTKHQw3tAABl2XCxi4mAU4zs3nE+01AHjM2uO/kd/hqXWy5cmfSQgeTw+Tk8UbP2DfxrXkXTuYaYaN6AXEx111ROfSaAxoNEfvjXJAyUdoQ+m2cyfbi2FzzVYeGvcowbHhPLXmKXReA5PqZrNn3X4G3vE38krvgiqldOC6wNVsyZzHzJR/o/fTsTN/LK/kT2eu9n+Mj/uUPvpUdkQ+SGL3wUgpEUKQkJDArpJRZEUN5wljN7rd2LJU36SYHozdn49ZN4R9583Dc5DFLM/rYn5Uc3sNPi4ybkYsH0Wf1J9xbazkF2Hk3+9cC+9A7YCx1PaKpqzIRe6e5nmQg5U8KEFix8vmQsXUV+tws7e0jhd+3g3AZWOm4WeYic/npqjoU6Lfvp0hElw99exZVkJZg56tftFoRgqeGJjKFxtW81L/waytacDp8zEtPKjda1osFgYOHMiGDS0rZXU/e1arthcNiuOTNfmcmx5DbXkJBYWFxPjVcXXqcNw6Pcnmjk0L7dy1E4D65SsIvOAChE7HFzO/YGnBUnoF98K2R3lAaf2PcER/jGj7TSViXhZZxWVkxIWc0GsdC0cyoh8FzBRCTKPREU4I8SGwXwgR3TiajwZK2zi2AIg/aD0OKDpeoU8E+kYPCpfXxf1D7+f+oc1WKL1Gj8vXusLUseCqUEY8EvjzK/fSv3Ifd2/czBqbiYXbdZRN7k24f+sfwx/mrObX3coErjwnjmijngH+frzSN6HVCMnrtZGf/z77S79jyxYDBcZ+yg4D+BdBXqQNgzYUnzAxdMhnWCwnxtPnALfXXcbL/h/jqxdsL4+j/54A3JoQLudqetT2IMObwaDsC9F7Ali4tXmUGDzASpWpnvFV8UAexeX+3Bn1LtdtLWCJt4jvR27lutV+DCrP5vnbx2H7eRtDX5akjY5jwpUDGThwYCtZioqKMBqNJIaGUtCYS720/FxCYr4hryKeffU1LK1oeZwGiUBi8oaQsOkxPvDbSP+w5rfO5IYSNhHNDTffikmnwefzsWrVKlatWtXiPNu2bSMi4hjzCD2mjMInaqJ5lH/x57ktg3d+3lHKzPQYNBo9cXFXYJutxe/TWzBo3aRO2EOFKYFLh73a1D6vBnoub35zzR7bH/Mh5oemTZvWpOivuOIKPvzwQ8JiWr+h9I8LYvcTB7KCtp6XOFHUL1rErv6N19NoGK3Vslv/atOoUnu0I/pjQWcgOb51n5wKHFbRSykfBB4EaBzR3yulvEII8S/gauCpxr9ft3H4WqCHECIJKAQuBS7rEMk7GEPjSLXWVUudqw6gKU0AArweL+Xl5RgMBjQaDUajEb2+2b3O7XWzIGcBwcZggoxBeKUXq95KSnAKXilZUFbD1nobuvLNWK/TMCW/joz92ei0HiL12xgYGMXKup5tpkfPtTtZpvegBzzdLHh8khy7ixy7i792jyHe1HKUXVOzkX1Z/wIgKQnKSpNwOq2E04cRL/2ZxVtuIEOsAAlbt93B8GFHX/LE7fRSW26nvtpJQ7UTrVbQLS0Us7X1iP/yyddj/FJHrlN5zc4y1gIxzMyb2dSmKjSTkNJh2A11OLV2jBYj5dl/wYaOJfpi7tPEkN+/JyaXk91J1TzufJzxlVEsGDqILUv9OecFLd+OGUyVuZCNKzWMu6wXmjYU16effkpNTQ133vJHIu/ryxt/2wTV5/JY0WRqe/kTpLudT33JBKBhDR6sgQYsQ3RE5p+FscBLwNAY5kwaBY9H8NDUW7j8x2/Y2k/JDfTD/G8wGAzodDp8vtYuu7H6Q5sBjgRTn6lMd0Vzdr9YvFKSW95AWb2TcT1antuvzx/gsT9QvWU99Rt/JHbmlUzJc7cq7QgQrNMeUskD6HQ67r77bl544QU+/PBDAMzmzk/aZRk9mugnnsBbXaVMxHo8SI8bPJ6mdY3VirHnya3ReqohZBuFm9tt3KzoZwghQoG5QDcgD7hYSlkphIhBcaOc1njMNBQbvxaYI6V84nDXGTx4sFy3bt1R3srxsb9hP5M/m9zu/gn2CYSUtHwlu/7664mNjUWj0VDSUMJZn7X2vr612/M886MDAP9xggeMTxBFc86Y5JwGkvLsrJd9+HnEu9x/dutJnyWVtVyamdW0PjHEn18qlYfR0qG96fW7bIper43MzTdTVaWMKE1+A8gY8AkWow5vnYu5i67j7sB7mSwXcC1vMXHC3qOym+5Zt5+f52zH52v+7giNG2v0ZvxjKphy8QOYrf5oNC1NFYV783jzwzkABHmN2HDiOqhJqamU5dFKGuX0oKms+HVCi+NfW/kgCWVucsPh9ofe5tIFW9B309PdbzVVm6ei9QXh1sBljwwiIqqlHTr7ggtxbN/O2qFDyUpOIsEVhH3HAoIMs6gK7sVe473YhscxO3Ib/luuJqa4+dqh16Zi7nXQ/76hHP7VnduS/0pRdTh9qwowSB+RkZG43W48Hg9utxufy0usK4hR7t4Y0RN590D0kYd3mXS6ysnMvB6XqxyNxohWY0KjNdO9+72EBB95dvi6Sgfv/7X5reL80EVYHvs71R4vNq+v6RNnMpDkd2Qmlb1797Jr1y7cbjeTJ0/GalULj5wqCCHWSynbTJx0VAFTUsolKN41SCkrgElttCkCph20/j3w/dFcpzOItETy9JinKbc3mkd+N5UQUhXC+pL19OnTh6qqKkpKSnj77bfp3r07qamp+AUqkbX9w/qzubzZZe6b7X8H7gU0PG+8o9V1K4P0xFWFETb5Gf7Sv1ebskUYmt8cYox6yt0eBgb4EW3Uk2huPYLWav0YOOAD3O5q9u//jqiomegaXeGky8vOvVNgEPiklvqGMUel5L1eHys/20tAuJmhM5KwBhuxBBv5MXMsPzINF/4s++nfrFs3ikurFNliegShM2hx1LvApwGNj2pta++LUfGjeGjmQ7yx5Q3e/6n16HdbTDe2xcCULXk0ePT8MjCKK1b5U0siAZpy7Hio1b9L3gsNFMgAZlPPxdVbGVZmoyjsFoab/k5WchIm7MQGvsnqcYMxZEZgcFZz0xI7V6Vmcb1vGHX93oMDir6nAVOP33kkWcIg43Je2fR/8EgVfHi+4qFx46IWzaTXh/QqPuxCp4yYc7ds4stnv0BrSOPqJ889aP4G9jvdXLMlm/OtuXSv2woIIiNn4PU0UF7xCzXV645K0R+s5AFqPv+C0OtvICH52GNEUlJSSEk5saY+lY7njI2MbYtpydPa3Zefn8961jNo0CASExPJysri448/5pGIHthrNPTYqQRTOHzuFscVWqtA48KiabvsYLfw2ehnPkmCrv1Jzr5WM8Xj04/aW0GvDyIu7vIW23ShZm68dCK89RYCyLj270d1zt++yqKh2snQc5OITgnEGmyixOHiT+LN5kYh8HBVc4Ro0Z5qgqP8qCqxEWDsTVRfI8npkej1enQ6HTqdDr1eT0JCAkajkcdGPsbZsbn84dWtaLUCj1cyY0AMNAYXrx/yFybu2MY2/NjdfQ/XlZnwszxGrE/LsKRw5oktOCvG4imdxv+C0vhfEHzz9V3kDbmY4QFQErKDNQVXMjfye24xNitxjQZ+qMihNvhiiv02MrYhnW7XtekkBtbI5uXaYghv/ZAWWg3ioDeWvWt/4+tnlVAar3M7OsOsFu1v2pbDxjobG+vC+QiIiDiHtNTnsdsLKf/1FwxH4OHUHkPW/h8A5f99idj//OeYz6NyeqIq+iPkgMeEx+NBp9Phdrvp2bMndoNiNtkTnUBoQQC7KxQvAKMLbvWcxXO5vUGYafDBt7/8jf7Sg9dlxeuycH3wVeh5C/Ly4PJ57V67weMl1+HCotXgp9Vg1WoxacQxu6nFxcXx98ceO6ZjN/2keJKs+TabNfOz6TkkEv9eQS3aXLJMMSuNvrgHO34txuSnoyRLsQ1biGTkiFQS+4cd8jrDExLIfqplvqFP0VL2yx30sc4lxb2W0U7F7XOd9gG+2T6cK5M2KP6PEkZ4G1gKXK/9njhRxsqZVyHL/DCH/0DmrsHUeXsSVbeD14fexc1L0/nn1fCX6koeCxdUZU2FtO8x2SNp19HXr9GU8w/lQSGTx7dTMBKcNhs7li9m8XtvEt4tkbL8XAIjgvALaPlwf7FPN67YnMWTiXqc26CychVSSlyuMgCMhqOz8d/+2sSmZXdhLzxlZRiS24n6VenSqIr+CBGAcDspLy6ixGrhg89fINjroafbRLk+hjF9g+j/acvERz22vMY34dkY3/2Ymz9Yx/wCKHHqMCMISzVwnvEVZlRlcmHUaMLtdoTJhBCCakc1hTV1LN7mQK/V8I/cErxRZmLd8PXyBpzAq/Wf8PaFNzZd65b4cB5LOfEz/tc+M5qdvxYTEGZm2/JC9m0ow7tmPw+30XbFPKWEYmCEmbRxsST0CyUmJQit7tiif2NjSnl85F28DlxbXcvoRuvPfK0Nf+CD7IFc0C0YUZoMCJYCD+uVicM3HP64dOdhMdbRo3wcAAOr7mRX+GrO1n7A7c4i5tjPpqF6Bucm78C61Yime/vzV3L47YiFzQF0c+rNXN9O2/9eO7tpOW3iVBa/+zph8Ymt2iWYjSwf1odt2/5MCZCRMacxL5Di0GY4SkV/MPrYWPRt+PGfSDYv+oGy3Gy0Oj0anQ6j2Y+MqdOPqx6xyrFxZin6nx6FdXOUMGcpAYnd0Y9Kz1+QGEFC6DWpmHu39oMt27cL694trN67hdXAd9OUVLqjdkXzg8/I9+U2ov01hNc1m2j2JY5n9g0jYeMuvrosne+W57Fox36KahyQ5cYbEcWGAaks3mpjuUYx/fQtyuLfW438q3INq4KVtAl6wCIlX+9oVjxG0TwavC9nDreumAcmC+j9wOPEJaPZv8IFMelo/Cz4T5lCwNRDJ2o7EvwCDAycqoy0UwZF4PP6qC61U1FYT87mcnav2U/qmBi2LVe8aGf/dQjh3TrGtS0iajrarE/wIngnKIC6qmuUmZSDzGIRsXXstGoRJRa8wWYmlzxDL205YS4DERorZb881uKcvcqGcW5Ef+51mEk21PCR1BG/OxGfTOXjZV/x3JIVDHnsYUbHRlL9+ecYU1IQJjPLS93c6ZjDBdrl2KURs/+5R3QPi999HYDolPa9QEr2Kw5smZk3oNcH4ms0B7YXnFZZWUleXh5GoxGTyYTJZCIiIqJD/PaPFSkli95+DQCftzkGJbZ33zYrmKmcWM4sRV+0AQwWSL2AA36MrmWVSGnEOiqG+pVFuIvr21T0UfGKcrNGRBHdfxBCvkK408hyn5I/I7GbhR5jY+nj0BCg1+GoqEV7UPcGjhrGZcCVr7/LY3ssZO6tQK/VEb/HQXLpfiZv24HUpPGPXol8u/vbJiUPMDw5hEdGCRYv+YpS21ZC+93E7If/j7R6B8E6LWlb1qCxRkBNTtMx9bvrqN8ViNGbh3NvFrb167EMG4omIABxlKXwDoVGqyEk2kJItIXuAyPwuH1NSh7AUe8+xNGHxuNy8dMbL1FRmI+UEmOQjX9GJeJqiID6eITVQHlDDJt9UBlbQ3lcCr8EjFWyMTVGb2ztP4wcdwN3f+EkKMhAQ70bjVGL9IHL7mGR2YUN+Ad24sLt7E3ri84nMfqgQXe1cpLdxWSuWELlo481yRYHREz4Mx8GKp5Wz4RF0h73fDofn8+L02bDUV+Hy2YjrFv7abCHD/uJ7OwX0On8cXtq8XhqCQwc2O6IfsGCBezZ07IAfVBQEHfddddhevjEUVVc2ELBA4y78npie6d2kkRnNmeWogcIToSz/69p1bbyB/CC3+Ao6lcWKWWG2sASpNhih517ARlTpjGTW3n9lqtIGlDElJtbe9NABJDCuVtzWJxXSa1/ApaGIj5/Zg6r+s3E3TOA/03rR/CWWooSr0an89DQEMRNy8/Fd5Cx9+1L4hnfvxeitpgfUOz/Vn0wvS1melvMSh6OypZZKV0+2L9BcS9M+uZbcmZfgmPrVnYPb/bYKEhI4pUnniPeYuL6uHBSzEYsuuMbAWo0gqk3prHq871kLsonOiWQuN6Hz6HTHtuX/cL25Yub1qN79saWG4rH5aI8bysel2K76W2xUtAnlV8GjG3zPDElOsBJdXVj0JunWQFNshu4uGAbnw03srKvUlHMoxEtImUBQiZNalL0prQ0HFu3MivBTPTENBweydn9Dl1HQKPRYrb6Y7Ye/u3GYkkmLe2Fw7Zzu93MmzePnJwcevbsSXJyMj/8oMREVFdXH/b4E4nb4WhaDo3vT33dBFbP1zLgbIlWd/IyaUqvD9EJiQJPNc4wRS8aTTbgddrg4yvwupREW6UvKFF/7YUVaBuDo5Z//A6/ffEJQqOhvqpSKQZ+CHoV2cn4ppp1g5Tydtt6vwIl4I3xIynIj0BtLb/tSye5+3r27Gn28PhU3kX9VB9UwZKlkFXTjdoExba8KDqfV9bsxF+nZYq+jtt/d819xc2+zZn33482JgZdY9GPA/iMJnY02Pm1zs7cEiW79CfpyYwPOb5QcY1GMPriHvSfEIclyHhc6XH7TZzCjpVLKNiuyN5n1DhCYuPx+Xx88X+PNLVz2jSErP+NpMj5JIYkM+fsOeiEjhq3jUvn38jgkL5c8tC9eFxe3C4vHueBvz72v7MUq20nM4s03GLvyRfd/AiO8ecLo4dqj5fUwix6l+TySUEY4//7EvZHH8PR2JdT330C3oXAiy4kYOTJT0z766+/snu3kgIhKCioSclD5+SLOpjI5BTu+XQ+AF/9ZwMNu6sB8Li8xzxHc7Ss/nU1i374icHmXoz84zlYLJYOT5F8unCGKfpGvB60T0YjJRjEObjkQUFKvrY1vcliZcxl11BTWoL0+fD5fEifj7Tx7QdZrf3mc0Y5vWxA8TAJrtzBH/xr+d9dzXlU5IR4RuffwafLfmZQ/3RGTxhPYKA/i5d80OJclY5g9nlD6K6tZLMxgGKHC5vXx2o09Lx1H2et/BvsWYjd3kDv2HqKh/qo3RiIfv53aA56ei0ZOIz/XnwV/+gVz7LuiTy0t5BvSqsBeD5n/3Er+gMc7B9+rAiNhvPvf5SXrlaSVP3yzuttttMIJ5aAEEIMXrZWbOXpNU/zwNAHCDMF0uDKJ6jbQMLiWgf2SCkJmB8H4ZcSDQRN7s5IvRZTj2DOl26+2l+F2LYKXX0NBW4Hb+/bR/LoUbB5Db2L67A6bAgkdYvWU+BZjpgYx/cmyYjuofSO8ie/yk50oAmT/sTbytesWdNi3XHQiLqzmXnXAL7bvoCcmiyefmEFKdgZZh/NljG11Md6mdBtQos04R3Fgh8XgIBVjm2selZJrXHttdeSkHDo6nFdkaOKjD1ZnLDI2Gd7gssGD+TCPxQ7/C8lyfQMerGpif+4OALP6ZiiI/++ZAYAATYnYQ02SgbFceszn9BmnoOD8HjqWLosA7O5G+FhU0lK+hM6nYWoxZtatOtmMpDncCFsHmY36Dg3LRq9dJG3aTnZe3ZisVi5/oYbeflf/0J0K+e1pBtwGZRJ3LlJoVyWU4FHQprVzMVRwcyOCiFYf2o9+70eN18/+wROm41+E6cQFBEFGoHX5cbZUE+39AxMflZe3PAib255s93zBBgC+GLmF0RaWtrSpduLp9qJNsCAxtj63h9rdENNT08nMzMT8/4CdJUlIPzw97ucMaseQBveB79RtxBnupi7XbfypW9Mi3M8dUE/Lh164kbYxcXFFBUVoRNafvt5BcW2cqZPn86QIe0Xtz/Z9HuvH4m1iQyqUIqv3OCYxAbLDv7W7SUu7nkxj4x45DBnOHpKS0v57rvvKCgowOtVSnU++OCDGNtII90V6LDI2NOeeiV3N6tfg/jhkP8bu+r60DOouYlsZ0R/rEgEhYNGUiR8XG+fwrbl+aSOPfSPXqfzb0oNfDDrR/TlPzklfFSsBCPlORSbs2HFfnZV5HDLI//FOcJAzWXvEFJVSXl5Oc+/8AIYDGhKI3D1UpS82ePmqpxKPI23+vXAFCyd6KFxKLQ6PRc88Nhh2x1KyYOSw6itwutCr0Uf7tfGEbTIV5OZ2ZhELCEFgzBi5Sx6Rpej3z8JXURfzFrFU+Y5w6t86Wip6F9YtOeEKvro6Giio5UU1+kDM07YdY4Vp1eZS8m3KhnLZ1crb8EfhCmmHZe3YxIG/p6IiAiubaM05pnImTWif2UklLbMoe3y6ih1fwQovr1B5yZjHXVsr5EHCkMHBQ5B4qOmZn2L/SkL3+brom+44/1DRyZ6PW58Xi96Y9uFSBq8XoqdbgJ1Wn4sr2XfljJm33l+0/7iZ29l8NhrWLRoUZOCmjhxImNGj+GznXu4P68Sm9HEtVFB3JAQRXe/k1fw5ERRWF/I2Z+f3e7+vwz5C1f2vfKozimlpKKigqqqKnQ6HVarlfDwZs+X+vpdrFl+AXnLJ+Gtmsrs/i/wjPkOFhQYKa93MaZHGHaXl/G9wvnjxPbLLXZ1pJR8sP0DntvwHJeETuF/+7/Dd9DczcfTPqZfeL9OlLBrcKgR/Zml6H0+cNa2Mp1IL0idBYRAYzi2ka2Ukl8WKzlAgoKGUV29ulWbzXN6cs6dD9J7SNveIQd49aYrsNVUM2D6n9ixSpkEnnlnBvF92s5z7fNJbvlrGsP2JHLOn84jZuwNSjw/8P7775OVlYVGo6Fbt24YjUY27CsEGcB150wlKi6M4KjTP4BFSsmEuROocFQ0bTs3dhzDEiczImYkEX7Hnj5A5fip+/lnqj//gvrFixEhwXz95yFk6JPouaqAqCuvxZymul0eL6qiP0Icu3bjralGFxbWVBGnPtyKrmA/RnQgpRIwY2g7L82qXydi0IcQFv1HFmX+RKLpEwAqdgRRvi2YsbMfOOTk7QFWzv2Q3z7/hOjet1G1Xxlt95sQx9hLmoNsfF4vu1YtY9+GtexatQwAjaEPOtNw/vTmhU3tvF4vBQUF7Nmzh927d1NXV0ekfQgNzQk0mX5b/8OmJDgdkFLy+G+PM3f3XMbHjefFiS+esV4WpxLuwkL2Tmr/ex94/vnEPPl/7e5XOTJUG/0Rkn3eeYdtE3rjDUTc03apMJ3OSk3tRmpqryex0RqypnAQhmU2AGUi8QgYNfsKRs2+Aq/HBxJcDg9m/5YPl+cuay2rz7UDl2snteVjCAhTRrBarZaEhAQSEhKYPHkyPq+PV29f0uI4S1DXmJwSQvDwiId5eERzQoZaVy0PLHsAsU4Q5Y3CZDA1JVHT6XSEh4dzwQUXqA+EE4jrd5W2fo8wHn3VMpWjQ1X0bWBOT0f6fDi2NFfgiX3pRQr/dAe+Blu7x6X3fxObLYeCSjt3z9sMCPLrYnks4yPO/9N7ZA8ewg7gnQt93PfX5YRbDm1OOOBvbNa3/iGMmn0FK+d+2GKb3mTG7bDzyztvMOu+tguZb1la2GJ9xrWJHZai4FRk3KfjsNgtTHZMxouXhB4JOJ1O9u1TJrtLSkqYNWtWp6YL6OpYRoygz84dSClBSqTLhXS7lb8uF7pjrbqlcsSoiv4g/IYMwbZ2LfYDHhZA7PPPYxk+DNHokiUO4X5oNEZiNEay2VnN3mqlmtK/xj5CiKkan7E5SOTazzX8Yn4V955SUmZdStpZA6itrSUmJuaIZR1+4aUMOGcm25ctojwvl82LfsDtUApHl2a39tgBeOXWX1oEhPmHmkgYduKyGUop8XjqcLsrMJu7IcTJV6ZxpjAaGrxN69u3b29aNhqNpKenq0r+JCGEACEQJhOYTn8HgNMJVdEfRLc5b+Otr8dXX8++s5QEYIV33YUpNRXHDiXNwMHV5ttjQJg/zhHhjBZLCTFVk+N7AufOKl5/eAb9f1rBz8PqmJc7EaKB1TB3w7vkacuP2sfX6OfHgLOVZFojLvoDK+d+REyv3iSmt66VChASa6WioL5pva0goo5i957HKSz8BJ9PefikdL+fhISbTtj12uPb879j9bNxhIlavrNcR3CkP8nJySQmJhIdHY2mA/P+HC37nW7u2pmHlOCn1WDWarBoNfjrtJg1GkwagZ9Ww7TwIKKM+sOfUEWlHdTJ2HZw7y+l/JVXqP70U8wZGVhGjsCQlIx1/Di0/kdu6rjhvbX8vKNl3fQdj0+k4qHm+8vXlPOjIZM/3nAPK+btxWjWYQ02MuicxFY5yzuKA//347FN71y1jKriQuL79muRkdBmy+XX3yYSGDiQ0JBxZGU/R2DgQNL7v4VeH3iIM54Yej38PU63JOv/pqE5jpQMHYLPpyTXE4I5pTb+XmnAqTGSZDbglVDv9dLg9eE8KJ7jzoRIHkyO7kShVU4HVK+bY0T6fHhratAFH3tirt0llUx5/tem9fDEQNbeMpqyOVvxub2szttESaSd4eNH4ikO5Ncv92ENNlJf5SRlUARTbzw1U7q+dccN1Oxvdt2JT+3P0PMuIqFfBvOX9McPe9O+kJAxVFauwGAIJyXlfsJCJ3SKwj8l+P4+WPNG0+oqYz8uGP5fALQCTBqNMprXCjw+KHG5eSApirsSj2wiX+XM5bgUvRDCBCwDjCimns+klI8KIdKB1wArkANcLqVsVWJeCJED1AFewNOeIAdzqij6jsLpLGVuSR2/Nuh4uU+3dkfRyz7Zza7VJVzxj+HMuW8FANc9Oxqz9dTzSijNyeKD+5WsnQOnnceuVctoqK5i/pQL2ZeYykTxI1fyLgDSqwN8CG1zpGlGn28IjT4Dfacfa/mAe8czFe8tL2P3+qj3+nB4fdh8PuxeHw6fD5+E+5Ki6GM9/txBKl2b43WvdAITpZT1Qgg9sEIIsQB4CbhXSrlUCHEdcB+0WWgIYIKUsvxYhD8enHY3RnPn2zaNxgiuTIjgcHGZ9VUOrMFG5j3V/JDTSu8hjug8IhKbJ3FTx01i6JDu1L53JffkL4d8eCnmIrT2y/D2+xiPS+KoMOF1a7BEOLCXhWEZcXKrHZ0yXPwuzLsGtAYq029m1sSHCLaeeROTbp+bWV/NIq8uj0t6XYKfhKSqQs63KtXBEBolsFGjh/6zwap65hwPh1X0UhnyH5jB0zd+JNALZaQP8BPwI+0r+pPCzp07Wb1aiUjNzs6mtycWU3AA86wPc11FLZMyroJpzxz3dVyucgyGjg0w8nl9ZGe29SzsGNPaffMyWbWvgkCznkCzniA/PcOTQ7lwUBzWNpJ5HQ0FO7aRvvoaLObmPO81y2rJ9FThWdUbEPQaMYa00eNJTB+ITt/5D99OI/V85QO0Hed8ZlDtqCavTvGv/3TXp03bz1n7P0y/tzL4PDD6rpMoXdfjiH7hQvGLWw+kAC9LKVcLIbYCM4GvgYtpqunTCgksFEJI4HUp5RttNRJC3ATcBMeeS3vr1q1kZ2cT21gbc6euEOoKSam5kOUaI8vXwFmBKxk1alTTMQ1eLzVuL36NxQn8dVq0QoDPC/WliviNZQcRGhwmKyuWD8Xs8DHynKxjqtbkzMrCU1qKNjgEjZ8ZfXQ0Gl3zvyJlUATmAANDZyRhsHSMUvw1q4LCajt9ov2psbvZXlzLgq0lvLpkH29dPZi02KO3maeOn8y2JT+z+N3X2evXh/GRWdT796QseCQXTz2H+c8/DQgueOAxkgYc1mKncgYR7hfOx9M+5qPlj/Fd3W76Sz3bcGF8sFhJ3yEl2KvgP71Be+qZLk83jkjRSym9QIYQIgj4UgiRBlwHvCiEeAT4BmgvBd0oKWWRECIC+EkIsVNKuez3jRofAG+AYqM/+ltRFD1AYWHLoCChaXZZzMzMbKHoL960jw21zUFQU0IDeL93FPxf214OJiAvdgbXFc6HtcEw878w8PDJsg6kSdVqteRecSXeysqmfQEXXkDsE09w84vj0Oo1HRel6fOBENTYPRRU2blyeAL/nJWGr9GjY31eFVe9vYZ3V+Xw7MXpR336s2+9iz6jx7Pmq3nkbc3kg2zFrTOml4dueTkAWEPDOl3JuxtsvHfbi7iD+uEfEILOqEWn15CQFsqQ6R2Tkrqj8dbUUPzoY+D1ogsPJ+Lee/BqNTz5xp/YV7SDaVMuZ7quH5aRIxCnaRxAv/B+jOx/Fd+tfIjNwk2I14cwHDQXYWuc0zG0nV1U5cg5qnd2KWW1EGIJcLaU8llgCoAQoicwvZ1jihr/lgohvgSG0mzy6VBGjhzJ2rVrGT16NFqttslH2ul0YrfbMZvNpKSkIKXklVsX4x9qYnp3C4F1NnbGGSgO0bGwohZEG4FLfmFgU0wr1xXOpwETZhxoqrJpWFeCt8ZFwKS230T27NnD3Llz0ev16LVaegUEkFRZyS/jX8ZR9R/Ym8kdTke72SqPlm3l2/jbir+xr2Yft1YNZmOIUoPqg99y+WzdDuwe5Yez6ZGz8EpJqOXYR0wJ/TJI6JeB1+NhyftvsunH7yjatZ2iXUpgUkRC5yrSkqwafnx9DU7zUHB4CUv1bzKTuRzeU1bR29aupe6gilF5n89jee9uWID+BFHw2nfkZ75I4rx5mPudmp5ZR8KOSqU8psYnqdRq2fzda/Sffouy0904ANOf/kn3OpvDKnohRDjgblTyZmAy8LQQIqJReWuAh1A8cH5/rAXQSCnrGpenAP/o2FtoZsqUKUyZMuWw7Q7knK+rcECFg9HA6B1KRZ71kwtAn4H34rehIgttVR5s/AD8QpG2cuqw8h9uBCBSU821ybdT9bqi1Gp/yoVLgyjJ38OgGec3jcy3bduG2+0mIiKCwsJC1gwfRmJObpM8ekMQujbSHBwrl353adNy/rYCetc+RqBfHJ9Fntuk5AEy/vETALHBx+/RodXpmHTdrUy67lZcdhs7Vy4jMjmFyOSU4z73sbJ9ZRGLP9gJaIlNgKi+STRUuXDYfBhMWgLDT11PFldBAQBxL/+Xgtv/yLrf+dEPzVNiM7SBHVMRrLPwNSjV3Q6kLV69fgmZuwow6gQRejsj0WFQR/THzZGM6KOB9xrt9BpgrpRyvhDiTiHEgXKlXwDvAAghYoC3pJTTgEgUU8+Ba30spfyh1RVOMkIjGHdZL3avLiGosI6eJuXVd3Gtm9R1qygPiSST+wGYdN4+0Oph3RwE4KDZDBSYmE790pIW5/7x389R7SolLD6BxAylms6mTZsACAgIbDIr+c96jdufGgtMPGH3eWn52VwdOpMSvxyW7/+GiFFQWB2Eflt1U5sgPz0Z8UEdel2D2Y/+k9vPDX8y8Hp8jUpeoTAXCnPzsAQZMVn1hMZZ6TPy1A1CchcUorFYsE6cSO/t29D++D3fvts8loqbPAXHvM/RRZ3e/vXDogfw4baRaPxX0c8bTBHJyFpFLW3HSg99N2JDO2+w0FU4Eq+bzcCANra/ALQqVd9oqpnWuJwFHL3x9ySQNjaWtLGxFL27Dd9OxV7e07OXhqJyCh0lYAKvp9EUM+YepQRhcCLWyEH8NWU8Xq8XU2O+DseuKjQmLUKvZVyxgb1rVhGfptx2Q70NS20yZls05SVawlFy0X+Dhzfv+55qreTeKT07tDDFsKhhFDUUcfWOmQBEmRM5u/ta5q48l4Ondjc+fBbBx2G2OZURAnoMjsDng17DIrEGmwiMMGMwnR5ZP9wFBejj4pryw/Q8ZwZ3T5nG1s++pSFnC4YaG+7wMDTtpMw+XZjYuxs/f3kddfVDyR/6f5D4KcuXKXNeGo2G6AfXN9VWUDl2To9v/QkkeGgUFY2KflGPcF4crGR93LLKS7GzCs8kH7rAOLhAKUzd1kukuXezo1xK7DBSBg9rWs/NrMbPFtfmtb2Nc67bi1vFmQFKKbtjycXSM6Qnq0tWc2HPPzOsvh+r/DdRXXIdnprQpjbJ4ZYOU/JZWVm4XC6EEG1+pJSEhIQQfBwRxkeLRqthyg2nr+26fskSAIoffgSNnxnh54fG7Eecnx+aHqlUfvAB+mhlLmlvfhlbSuoZ1iMKs16L2aDFqOvASf0TTNS9Q7Buiya//Cn27Guuc3vfffd1ai6irsQZr+hNPYIJvrAHJfv28GpY82TqruB0LH2vQac9ui9aaW4t855cR/KAcM65uR+9hkWx8I038TrWordejFYfT0HgLuYHFlBXO5IXJyZw7lnNEaL79u1jxcrPyc2pxedr/vf4+/tz4403EhBweJvsfYPv47b026h11VLnquNqdz1l9fXkFFuw6sKpc3gYnNgxSjc3N5f333+/zX26mgoMlaVoHQ3YoxN56Pn/dsg1f4/D7eX1pVks31PGG1cNJtDko7JyGaGhE9FoTu+veN2SxcgGGz67HX7nXx44axYAk19eA0C0cyn7DSH4GrOEPnlBP/5wAmvVdhRCr8E/oxuT2ENU9DbmzZvHjBkzMJtP3TmU043T+1fQAQi9Bkv4PpK/m4F73NKm7aNu/eqYRkQf/e1hfO597F13FdzcD61OQ0KqIGs9uFnMwu6ZFAf2wuu6kUVrfiTh3r+0uE5O7hTi4iAoMISNG5sdmerq6qipqSEgIIC3CspYWF5DgtlIgslAgtlIotlAotmIv06LEAKrwdqyGHYk0P2YuuiQLFy4EIDBgwczcOBAfD4fUkqklMy777amdubinA6/9oI9pVxbUASAbmcVugIbA//5Ey+PmY/JrMiVkvIgCd1u6PBrn2j67NzRYl1KiXQ48Nnt+Gw2nDU15DocaIuLuatiDReFTgZjACX2HP6pCccH7N9dTUlYIKGxVvTG08MFMzU1ldjY2CMa0KgcOWe8ogdg36KmRat0kTl20DG/9gZHmanIh/P/PKhp29LB5SyKyEUj87m4NJT7E9LQT50Ef5zU6ni9Pg63u4C60qEttp999tnExysxaR8VVVDgcLG13k6lu2WKhAiDjvkDe9DNfHKqRoWHh1NYWMi6desoLi7m/PPPJyxMiRrW6hLxenLwGUykzbrwMGc6clw+H7duz+W7spqmbZ7UYDypwUz9+CNycmz07qNs37v3SSorltO377MYjeHtnPHURwiBMJvRmM24rFbenDuX2tpaNMB1oc1l+qLMiUypdisrK8r5fIXiEmz9Uz5Xp17dCZIfPUFBQZ0tQpejS2WvrHHWUGpT3M4EAo3bRqLUoREHmV8OKHApISCaW5bfz8qilQB8l19EyDkv4UqayJYt95Ka9kfCQocf9/2sKlzFzT/f3LSewSQ2sYjrq2u4yy8Fht4M/pEQPxz0ygSv0+lk+fLlpKWlEXWQZ4WUkokfrKXWrOGJ8SloPF7C/Ex8VFzBR8XKXMPGkX2JPonl2davX8+3334LgE6n469//WuTbfXDTz5k78693HTTTUdVWOVQrKyq48JNSnGVzzK6sy1nO49WGzFKB3fwLBlsbPO4YUO/x2rt1SEydCYlJSW89tprpKamkp+fz+zSZrv2Sn9BeX7L2MU18d8x/rw0rk279oTLtqa6noUVtfhrtZi0ApNGg0mjIdnPyJBA1R/+RHLG1Iy9csGV7K2vpTryEXSuvVirP+WOst3cUt32ZCcRfVlpaS7E8XfdeB6IP5cln19Pcvf1ZGb+ysBR2+mzagdj/Sr4dOgEhGhps1/xyQfs+nUZqWMnMfzCS39/BQBGxo7ki5lf8MKGF1hXso6gmm/B4ke1RguF6+HLxoIcEx6CcfcBSvWjyZNbF1TevmMNF61SqlftW7QBgPBfH2LH8y8COt5NSzqpSh5g0KBBDBo0iPfee4/s7GwKCgqa0lgMzhjM3p17qays7DBFPyrYn4sig/lsfxUXbdoHjS6vc7i8qY1W44/XV9fiuAZbVpdQ9HmNNVhjY2Opr6/nrVrljTTmwtk8Ut6s5H8c3JN0fz9uP4EuvAfzQVE59+0qaHd/0fh0NKfJBHFXo0spes2OLCqHK3VUXbqhVPoNZa3jL9zcLRGRMArWvwOuBijfrRww/gHW9pyKVqPF6xU43ZJAPz3ZiZuoqQmnrDSJ4EFKoq5ltlAKVnxGeNRZmHoEI30+akpLKf95F9LhpiRr7yFlu+CbC5qWhzqcvFSqvFLLh8oQRRtgzlQwHH7Ek7/B3WrbvviB7K238feM3pwdfvx53ncU17KntB6DVsOQxGBCrUdmBurTpw/Z2dnMmTOHhIQE0tPTsdmU6Mbs7GzS0jrOC+alPt2YFRnMD2U1LKmq5aHkGAz1/8SV+zD+AQPxemqx2RRFL4SOfmkvER5++GC604ED5RAPzI8AXH755cTG6thd9jofCmXkHmU4ucnjZoQHtavoBwb4qUq+E+lSiv7J97xMDqvFm9I8keP0G4zImALOOihqfKU3h8BlcyF+CAeSDug1YGr8Xfj5hRMRMYBZ572IRqPhk/DfqCj6ABY+TrncylrTIrJ2rCPSnMj4qEuoc1cSdcUQWlGcCYHx4BeCVWOk3ucEoI+redQlnowDfaN3QWDL1L01zhruX34/q4tX88jwRzi/x/kUju3JuuI92EyCbfFGjDoN3WLvhHo7l0eHcryU1jo454XlLbadnRrF5cO7MabHoW3cQ4YMISoqirVr15KVlcU333wDgMFgYNCgQYc89mgRQjA5NIDJoQdN2kVeRpamgsLCj/B6G0hNfZ6oyHM79LqnArNnz2bDhg2EhITg9Xoxm810794dIQR/HnA99bkOoqyxRBhO7s87WK/jI3khEthGP+rwJyf4DlKD47k46kzO1dn5dCkb/eLPRnLtur8xZPxOfjOMxiP1/LfkAS7ctwF8jSPhmS9BxhVHFYRRV1rNv195HoAAnQW2LEMiseqCGRt1EZmVSwgf05NJ193adEzZxvdYtehBKrUaZty8kXC/cChcT21YCq+v+RdTq8pJC0hE47aBs9F8NPlRMDWPyG/75M8sd/wEjQOhLVdvYfTqHey1OVvJ+MduETzU/fhMIw1OD4Me/wmH28cjM/qi0wrmrMgmp8LGqJRQPrrhyOcrpJSUlZVRXFxM7969j6oW7vEipQ8pvWg0Z3A65E7E67UhpUSjMZ727q2nE2eMjd4Xsp8b+73Pm0uuREs5WiA9LQfSLwEEnqQRzLUkMshmZ5fNzVf7q9BpBP/pFY9V17772Zot65uWzaFWbI054uu9dhZW7GXkhWcx8Jyzmtrk1+UzbfOzEB6KwWOCtT8zPXE6YfEDCdAI7hvz+GHv5Yc3fiRlSR3TSlNxPTeLcSnK+X8e3IvvyqrpZjZi8/po8Hpx+SRnhR2/O9qkfy/F4fbx2hWDODstigVbismpUEwvV49IaNHW46nH53Oh0/m3qVCFEERERBARcfILRgihaTWXonLy0GrV3DSnGl1K0QMMj17H8GjlbcAnBf79XoKIcwBY+kt3/lzxOeTtaXHMP1JiD6noR44ciU6nIyUlhdjYWGyXX0bhrmx+fKuC9InxDJ7eA3FQ0encWiVhmdar57q1T2NbC/NYR69hUUy+tu8h5XfaGlg590O2LVK8WHaFAo9/yNC/94PeoZi0Gi48Qa/BGbFu/KO+Ym/2Rj4utbMjP58AwzmEerzUb/8rOzTROBwF1DfsweVSvJv8/fsxdMhXJ0QeFRWVjqFLmW7c7moaGvYipQfQoNEaCfBPQzRGCublv8PQvc1pewb4+3FbtwjOjQjqIMmbqXXVYtFaef32JYyxavF6a1hlc3Pb6+37ky/54G3Wz/8SgIyp0zF4JWt+/h6AK59+sUX5vhPB0pXT8Th3HrJNgH9//CzdsVh6UFj4MXp9cKcq+qxNZXg9PqK7B2Iw69AbtadN6L+KSkdyxphu9PoggoLaL3LRLf5aStqrg3WcbNy4kfLycs46SzGxBBgUU8rUtACMBXbQBRPv2XLIc3jdzR41OZkbqC4pxhoSyvn3P3rClTxA/76PsnX7n9FozDjsWa32jxm1DoMxGJ/PTUPDXvLy3iQkeOQJl+tQLHitZZ9qNAKPqwgh13Llkw8TGHH8E9QqKqc7XUrRHwlej4etT35FRFgSOq2e4It6ogtpXfDDKyVOn2wqMXgoPB4PX3/9NUjBoD7DMVoN7N1QSliclbhZvSj77yaKDcVMfOCKQ55n0nW3MOayq9m44FtWfPI+Or2BP/zjXwSEnxw7d3DwUMaMWgGA3V7AmrUz0OmCiI2Zjb9/GvuynqGubhv1DXuQUvEcCgzsWG+ao8USqMdd62bYRT3weH04bR5Wf7EYr3MvG77/kgnXnH7pD1RUOpouZbo5FDa3jVc2vcI0OY6ALxxN24MuSME6NBqfT/LqbYsBGDnRxnlZRvr0DOHHCw6tyEr27eGjv94NCIyBNyM0zRNR8X2CmXlnqwzPRyZvbQ32ulpCY0/QK8gRsCnzBiorV6LVmvF4atDpAgjw74+/f1+s1j74+6fh55fUqaaSLa9mEpxbi09KahMCKTRo2frTAwCEhMdx7X9b1cNRUemSnDGmm0Nx95K7WVW0ivd4j+cDrqd/9AR0IRYsg5X0AraaZt/2uxfvRxMYxa41JZROdhAR0H6Jv82LDtRRafnADI2zMvGq9ide59x1M7G9+3LWTX9Co9HgcnhoqHYSHKUETfkFBOIXcPzBT8dDXNwVaLV+6HUBBAUNJSJiKhrNyXOTPBw+h4eQUhtevQaN20dQXi0rqt1EWEczIiQDk9aCfXsF5r5HZ775oKicR/YUMiLISoBOi79Oi0WrwarVEqTXcmFkMMH6M+ano9IFOGNG9JWOSsZ9Og4AnZRsDBoHs15u2i+lpLygHp1Bw4MLd/LDthKuHpHAwzP6HjZVcfX+EoIij7zSz/6svXz44F0AGPz/gEbXstJReDd/wuOtjDg/BZO1pevigf+XOuEI1QuyqV9aQOCVfdj//nZMbfRJ8AU9sAw98v/NngYHY9YoE9Ipfka8UlLv9VHv8WH3KcWqn+kZx1WxYR1zEyoqHYQ6ogdCTCFsuXoLDR9fgmfvQpjS0nb77UuZ5G9XkoK99trR5QY5GiUPEJmcgn9YOPVVEqFtHW1aV78Zi997rN8YQFTMJDblbCXY1IBZW4HNlkWocTQ9TXdg6t0bjcWCz+nBua8GXYgJXZgZoTszfMjrlyrh9p491Zi7B1GdV0Ggp/krHfHHDAxx/kd1zh6W5re3p3vGMSq4+fhVBVV8Omcr3k25/GjOJciZydDUAoQlDCxh4BcKAbEQ0fs470xFpWM5YxT9ASyXfdrm9h6DI8nfXolR42pzf0dz08vvsPqbLKoz1yJ+egWtNoUGv0jy4ydjDMrHFFyIw13I6B2PYlzRE4C7Br5NvzCoX7+S3FeUYhOG5GQ0AfFg6IM+ZiAI0AabEALMGREEnpVwKDFOWw5+E234rRhoWf3rXzHv8nzsW8d07o0j+zJg1Xau3pLNvIwUgvVart6cxZBfy+lZ5MZGDUpmo0QGVf4Fnfhd/qFrF0BC53ojqagczBmn6Nujz8jok14setjMZJiZzLbb0sityWV1/gZ2rf6Ei8OUlAheTQLGFaVN7Z/fcD0jo1dzZ/YnTdtcWVlAFsKwAX3MQCzDovHZPdgzy3DuroIuquiFEMQ9NQZQJtpvfvMqxtQOpExfxS8Bayg1VJJdk01y0NG7pUYbDQwJsLC2toFz1u9u2j50fzmu+iVodPHojb2YHvIfwMj+0a8RmZEB6+bAby8fUXI6FZWTyWEVvRDCBCxDyQWrAz6TUj4qhEgHXgOsQA5wuZSyVT5gIcTZKEXEtcBbUsqnOk78rkFqaCqpoalMS56GfWgeq36dgCVgPNKtw6Sz4/A0l1TbVZWCYWdr08zyESH4BdewY3sh/QK1DA6OR29Q6rUKIfB6vWi1p0eVoaPFT+/HiJET+Sbre3LrlKjk7oHdifNvu1bvkfDNwBR2NDiYuHZX07aorLfxAT73Pjz2JXxWbeGePvVErrgehu0Gc5DSMOTExzyoqBwNh52MFcqsn0VKWS+E0AMrgDuBl4B7pZRLhRDXAUlSyod/d6wW2A2cBRQAa4E/SCm3H+qaJ2Iy9nTAZsvm199a56AHSE19Hr89weT+8UZ0DtCaJF6HMvn4w0DBu1M0ZObk08/xFnGijAl5C9F6lBTLztAoXBGK0gv3+pNSAQmXD6b3qHEn58ZOU6SUjF69lX12L0/LOyl/vbW9/+aU37DqDzLdWCPh3t2t2qmonGiOazJWKk+CA9U59I0fCfRCGekD/AT8CDz8u8OHAnullFmNgnwCnAccUtGfqQjR+t/Ru9cHxMY22nsjod+mxlqijwUiJUgfaNP/wOydcwHYbLyB/+wc03T88Av/QGllJZklFQCUaesoi4CA/KITezNdACEEK4f3A0DKX/CMrsdW28BbGxezdW0O0yv0+DRa5PmXI1y14KiFqP6dLLWKSmuOyEbfODJfD6QAL0spVwshtgIzga+Bi4G2IntigfyD1guAYe1c4ybgJqCpOtGZhtkcz6SJ+5BSUlQ8l7q6rURHtxOw9VgNomAd4q1J9CzfCh5vm81GXHQpGo2WymefJr/eDkBggxtTkLXN9iptI4QWvSGQwLBA7jnrcopSi/B6vfhH3YPQd02TmErX4YgUvZTSC2QIIYKAL4UQacB1wItCiEeAb4C23FXacvZu01YkpXwDeAMU082RyNVVEUIQG3MJcMmhG/pHK5+SxnwvkWmIyX/nbr8wVi5YxJqfFrP84/eI6dWHsePGAtBQVcWit1/BZGnbRKRyZHRUWUQVlZPBUXndSCmrhRBLgLOllM8CUwCEED2B6W0cUkDLkX4coNoMOorAWLhnJ3g94KoDUxAIgQYor/gagHXffgHftj7UP+zQ1aJUOp7snJcpL/8FrcaETueP3hCCKT8Fa0M6RmsYwqDF1CsYfaTqtaPSsRyJ10044G5U8mZgMvC0ECJCSlkqlAoPD6F44PyetUAPIUQSUAhcClzWceJ3DRxuL5d+v4WCYC1rx6QdfdSrVgfm4Babpt9xH9X7S9psrjMYCY5WR6Qnm6LCT5D4MJlisdlzcGfvIOnXc3Biw4lS8NuVW0volYeuWaCicrQcyYg+Gniv0U6vAeZKKecLIe4UQtze2OYL4B0AIUQMihvlNCmlRwjxR5SJWi0wR0q5reNv4zRFStj5HaZPL+f/fAlc4HoM35g0jtbiO3fXXP752z9JCUoh0RhHgNbKxF5TGZc4/kRIrXIMeL1OHM5ikpLuIDnpDgCy3nsfKTyE3J2EX1gChY+sQmNRyx+qdDxH4nWzGWiVglFK+QKKf/zvtxcB0w5a/x74/vjE7ILkrYbv72myr/fV5PL4xGC0x5DDpsxeBkDB/mxGL1Jc/daxhb6XVhE2YwZCryqPzsbhyAckfubmADbXliCExotfeAJ4fODxoQ08dZLGqXQd1MjYk43bDl/eAtu/arm9/6VcNGXCMZ3y9ozbMWbuoXrRXtakj8alNzBiwxJyH32Uqn//h8i/3EfgzJnHL7vKMWOzK4FcRmMMVdszqVq6A6suFlCKmXsbs6dqg1RFr9LxqIr+ZLPwYUXJp10EWz9Tts14DgZfd1ynHf70Ql6cOYOlI84G4NfBE7lBVrH/9tup+ujjDlH0y3aXkVVWT6CfnkCzHqtRj5SSarubpDALPSOPLoHYmcS+ff/CVJ1Cw9MOwIEBRck7tPXU/1KAK78OOLSi91RUgM+HMBqVj8GgZjFVOSJURX+y2dHoAjPyT82Kfv7dMOhaOIYfrXS7qf7sM7Q+SVBtZYt9fmNGowsPx56ZebxS4/NJbv1wPQ2utv31AbKfnKYqnnYIC5tEbt2bTevCrEFotJhdAdT+rEzEohPoI/zaPN62YSO5l7Xtx2AdP574117tcJlVug6qoj/ZzPgPfHIZvNEx6Qfyb7udhuXL0VitXPvLSi7SL6NwgpVJY+bhXLgQT2kpuqijS6PcFnmVNhpcXh6a3odJfSKpsrmYn5nHnJVKquCXLxuoKvlDkNL9PlK636f4rP0O6ZNIt1dxjTW0PRXv2N5+MLkuMrKjxFTpoqiK/mSTMhmSJ0CWUraQuCFwyYfHNJoHsP32G4bERJK/m4/QapFuN71++JHyG+7DuUNJlxD/6ivHLfbTPyjFOLw+yXurcnh3VU7Tvu/uGE1qTOdWwzqdERqBMB76p1jyzjuUhQ/AWl+I0w8iz5pIxNix+E88tnkdlTMLVdGfbHRGuOqrDjmV9HqRbjeunBxs69cjNBpKn3se+/r1GJKTCbr0EoIuuABTnz7Hfa2rRiSyYGsJTy7Y2WL7j3eOpFf0qavkXa5ylq9Qsm707Pko8XFXdbJEx8Y+42DyekxBCB83vzwJrUZ9e1I5clRFfxrjystrWs676uqm5ai//52giy5EdGBa4hHdQ5l8VhI//5SNJ9YPT1owY4KtJEWeukr+95SV/khkxKW8++672O12XC4XDQ0NGI1GEhISmD59OoGBp+b95HWbAkCfUXGqklc5as6MmnNdFGNSEr13bKfXxg1YJ0wAvZ6Ejz8m+JLZHarkD1ATasAxNRZPmhKFu7yqntfzyzr8Oh2JwRBGn95PER93DQmJt7J3716KioqoqqqioaEBAKfTye7duykpaTuS+FTA6KeMyfwCDZ0sicrpiDqiP80RQiDM5g6xwx+OT9O7s6amgSy7k1y7i+/Lqunnbz78gZ1MTMzFTcsbN/zctDx8+HDq6+sxmUw4nU7i49tKwHpqcMN/xna2CCqnMYctPNIZnKmFR1ROPFJK9u3bR2JiIjqdOs5R6TocV+ERFZWuhBCClJSUzhZDReWkotroVVRUVLo4qqJXUVFR6eKoil5FRUWli6MqehUVFZUujqroVVRUVLo4qteNisph2N+wn/X712PWmTHpTAyIGIBJZ+pssVRUjhhV0auoHIYXN77IN/u+aVqfljSNp8c+3YkSqagcHaqiV1E5FP/ujXGHhgs0ghWTwil1VDA6dnRnS6WiclQcVtELIUzAMsDY2P4zKeWjQogM4DXABHiA26SUa9o4PgeoA7yAp73ILRWVUxFfdTEXr4gBJJcu28/sB7SEmkI7WywVlaPiSCZjncBEKWU6kAGcLYQYDjwD/F1KmQE80rjeHhOklBmqklc53dD8s4b4154HYG80RBqDSA1L7VyhVFSOksOO6KWSDKe+cVXf+JGNn4DG7YFA0YkQUEWls7GOn0qfnTvoA5zb2cKoqBwDR+ReKYTQCiE2AaXAT1LK1cBdwL+EEPnAs8CD7RwugYVCiPVCiJsOcY2bhBDrhBDryspO7dS3Kio1C7Kxby3nVEwKqKLye45oMlZK6QUyhBBBwJdCiDTgJuBuKeXnQojZwNu0WRGTUVLKIiFEBPCTEGKnlHJZG9d4A3gDlOyVx3Y7KionHp/NTd3Sgqb16IeGobWqeeJVTl2OKmBKSlkNLAHOBq4GvmjcNQ8Y2s4xRY1/S4Ev22unonK6IMw6Qi7v3bRe/PhqPLXOTpRIReXQHFbRCyHCG0fyCCHMKKP2nSg2+XGNzSYCe9o41iKE8D+wDEwBtnaI5CoqnYQQAr9+4UT+Y2TTtpL/a+VwpqJyynAkppto4D0hhBblwTBXSjlfCFENvCCE0AEOFFMOQogY4C0p5TQgEsXUc+BaH0spf+j421BROfnoDc3lGu0+L1JKGr/rKiqnFEfidbMZGNDG9hXAoDa2FwHTGpezgPTjF1NF5dSk7LIgFnyxgLCYYHqI8Z0tjopKm6iRsSoqx8GA/v0Y0L9fZ4uhonJI1OyVKioqKl0cVdGrqKiodHFURa+ioqLSxVEVvYqKikoXR1X0KioqKl0cVdGrqKiodHFURa+ioqLSxVEVvYqKikoXR5yKaVaFEGVALhAGlHeyOKcqat+0j9o3baP2S/t0hb5JkFKGt7XjlFT0BxBCrFOrUrWN2jfto/ZN26j90j5dvW9U042KiopKF0dV9CoqKipdnFNd0b/R2QKcwqh90z5q37SN2i/t06X75pS20auoqKioHD+n+oheRUVFReU4URW9ioqKShfnlFT0QogMIcRvQohNQoh1QoihjdsThRD2xu2bhBCvdbasJ5P2+uWg/d2EEPVCiHs7S8bO4hDfmaEHfV8yhRDnd7asJ5tD9M1ZQoj1QogtjX8ndrasJ5tD9E2oEGJx4+/pv50t53EjpTzlPsBC4JzG5WnAksblRGBrZ8t3qvXLQfs/B+YB93a2rKdK3wB+gK5xORooPbB+pnwO0TcDgJjG5TSgsLNlPYX6xgKMBm4B/tvZch7v51QtJSiBgMblQKCoE2U5lWi3X4QQs4AsoOHki3VK0GbfSCltB7UxNbY702ivbzYe1GYbYBJCGKWUzpMsX2fSXt80ACuEECmdJVhHckp63Qgh+gA/AgLFvDRSSpkrhEhE+ULuBmqBh6SUyztN0JPMIfrFAvwMnAXcC9RLKZ/tPElPPu31TeO+YcAcIAG4Ukr5ZacJ2gkcqm8OanMRcIuUcnIniNhpHK5vhBDXAIOllH/sHAk7hk4b0Qshfgai2tj1N2AScLeU8nMhxGzgbWAyUAx0k1JWCCEGAV8JIVKllLUnTfATzDH2y9+B56SU9UKIkyfsSeYY+wYp5WogtfFH/Z4QYoGU0nGy5D4ZHGvfNB6bCjwNTDkZsp5sjqdvugqn6oi+BgiSUkqhaK4aKWVAG+2WoNij151sGTuD9vpFCLEciG9sFgT4gEeklKf/JNIRchTfmcXAfWfKdwYO3TdCiDjgF+BaKeXKzpSzMzjc96arjOhPSa8bFDvZuMblicAeACFEuBBC27icDPRAsUufKbTZL1LKMVLKRCllIvA88H9nkpJvpL3vTJIQQte4nAD0AnI6Q8BOpL2+CQK+Ax48E5V8I232TVfjVJ2MvRF4ofEH6gBuatw+FviHEMIDeFFsipWdJGNn0F6/qLTfN6OBB4QQbpQ3nduklKd7Otqjpb2++SOQAjwshHi4cdsUKWVpJ8jYWbT7mxJC5KBM1BoanR2mSCm3d4aQx8spabpRUVFRUek4TlXTjYqKiopKB6EqehUVFZUujqroVVRUVLo4qqJXUVFR6eKoil5FRUWli6MqehUVFZUujqroVVRUVLo4/w+iws9guuUB6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#finding the PRECINCT multipolygons to clean them up (like we did for Ohio)\n",
    "print(type(vtdGeom[0]) )\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        print(p)\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "cf9b8c1e-ac09-4f3b-9813-458492786c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SKIP for MOST STATES #from below, there is a problem geometry near -86.9, 30.39  #FROM PREVIOUS STATE.  UPDATE, PLS\n",
    "#let's plot the areas around these. \n",
    "targetX = -86.9\n",
    "targetY = 30.39\n",
    "rMax = 0.2\n",
    "for m in range(nPrecincts):\n",
    "    tractX = vtdGeom[m].centroid.x\n",
    "    tractY = vtdGeom[m].centroid.y\n",
    "    r = ( (tractX-targetX)**2 + (tractY-targetY)**2) **0.5\n",
    "    if(r < rMax) :\n",
    "        if notPolyVTD[m] == 0 :\n",
    "            x,y = vtdGeom[m].exterior.xy\n",
    "            #plt.plot(x,y)\n",
    "        else :\n",
    "            plt.text(vtdGeom[m].centroid.x, vtdGeom[m].centroid.y,m, fontsize=9)\n",
    "            for geom in vtdGeom[m].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "2090f30a-dcfc-4fce-a79f-09f1982d1221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#STATE-SPECIFIC PRECINCT TRIAGE.  OK, based on above, make 2561 its convex hull.  same for 2589.  2592 is squirrely also\n",
    "badPrecincts = [2561,2589,2592]\n",
    "for p in badPrecincts:\n",
    "    vtdGeom[p] = vtdGeom[p].convex_hull\n",
    "    notPolyVTD[p] = 0  #this is no longer a MultiPolygon\n",
    "#from below, there is a problem geometry near -86.9, 30.39\n",
    "#let's plot the areas around these.  Start with blue in NE Ohio\n",
    "targetX = -86.9\n",
    "targetY = 30.39\n",
    "rMax = 0.1\n",
    "for m in range(nPrecincts):\n",
    "    tractX = vtdGeom[m].centroid.x\n",
    "    tractY = vtdGeom[m].centroid.y\n",
    "    r = ( (tractX-targetX)**2 + (tractY-targetY)**2) **0.5\n",
    "    if(r < rMax) :\n",
    "        if notPolyVTD[m] == 0 :\n",
    "            plt.text(tractX,tractY,m,fontsize=9)\n",
    "            x,y = vtdGeom[m].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[m].geoms:\n",
    "                plt.text(geom.centroid.x,geom.centroid.y,m,fontsize=9)\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 56 grids for 15 districts\n",
      "starting grid no 0 at x = -84.551455, y = 38.659072 \n",
      "starting grid no 5 at x = -84.55145499999999, y = 41.215562000000006 \n",
      "starting grid no 10 at x = -84.01375499999999, y = 40.192966000000006 \n",
      "starting grid no 15 at x = -83.47605499999999, y = 39.17037 \n",
      "starting grid no 20 at x = -83.47605499999999, y = 41.72686 \n",
      "starting grid no 25 at x = -82.93835499999999, y = 40.704264 \n",
      "starting grid no 30 at x = -82.400655, y = 39.68166800000001 \n",
      "starting grid no 35 at x = -81.862955, y = 38.659072 \n",
      "starting grid no 40 at x = -81.86295500000001, y = 41.215562000000006 \n",
      "starting grid no 45 at x = -81.325255, y = 40.192966000000006 \n",
      "starting grid no 50 at x = -80.78755499999998, y = 39.17037 \n",
      "starting grid no 55 at x = -80.787555, y = 41.72686 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 825361.518658198 4791648.786793172 52.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/2 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 15   #Ohio congressional, ~1min to run\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9869000000273387e-07 2.3645385499993457e-05\n",
      "0.002628656386616965\n"
     ]
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "print(tinyR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 3168 tracts\n",
      "loop no 9.0 for tract no 6 with population 640879.5136633685\n",
      "loop no 10.0 for tract no 6 with population 771229.9789983974\n",
      "loop no 11.0 for tract no 6 with population 783352.7909225773\n",
      "tract,wide, thin angles are  18 128.8418 51.1582\n",
      "loop no 9.0 for tract no 18 with population 783850.40167028\n",
      "loop no 9.0 for tract no 19 with population 773537.8891660004\n",
      "loop no 10.0 for tract no 19 with population 787130.3557680747\n",
      "I am working on tract number 20 of 3168 tracts\n",
      "loop no 9.0 for tract no 20 with population 646748.7690301423\n",
      "loop no 10.0 for tract no 20 with population 693436.1302893229\n",
      "loop no 11.0 for tract no 20 with population 749005.3257728425\n",
      "loop no 12.0 for tract no 20 with population 769539.6425720503\n",
      "loop no 13.0 for tract no 20 with population 776968.2242722994\n",
      "loop no 14.0 for tract no 20 with population 781286.3982546859\n",
      "loop no 9.0 for tract no 22 with population 751317.1309671811\n",
      "loop no 10.0 for tract no 22 with population 786770.4599037622\n",
      "loop no 9.0 for tract no 23 with population 755849.2977310258\n",
      "loop no 10.0 for tract no 23 with population 775652.2296748662\n",
      "loop no 11.0 for tract no 23 with population 783552.7085496155\n",
      "loop no 9.0 for tract no 25 with population 784618.6953093078\n",
      "tract,wide, thin angles are  27 143.3431 36.6569\n",
      "loop no 9.0 for tract no 27 with population 660091.0549223201\n",
      "loop no 10.0 for tract no 27 with population 709336.7740384568\n",
      "loop no 11.0 for tract no 27 with population 755679.943531492\n",
      "loop no 12.0 for tract no 27 with population 771764.7424766101\n",
      "loop no 13.0 for tract no 27 with population 780015.6564255715\n",
      "loop no 9.0 for tract no 28 with population 782658.8559839306\n",
      "loop no 9.0 for tract no 31 with population 765574.18896341\n",
      "loop no 10.0 for tract no 31 with population 778864.8298433045\n",
      "loop no 9.0 for tract no 35 with population 756975.2815179066\n",
      "loop no 10.0 for tract no 35 with population 780641.8942150048\n",
      "tract,wide, thin angles are  36 92.6183 87.3817\n",
      "loop no 9.0 for tract no 37 with population 782977.7316258\n",
      "I am working on tract number 40 of 3168 tracts\n",
      "loop no 9.0 for tract no 56 with population 760789.9373465977\n",
      "loop no 10.0 for tract no 56 with population 784174.5824450506\n",
      "I am working on tract number 60 of 3168 tracts\n",
      "loop no 9.0 for tract no 62 with population 770834.5367777812\n",
      "loop no 10.0 for tract no 62 with population 786812.7315557757\n",
      "tract,wide, thin angles are  63 110.7038 69.2962\n",
      "loop no 9.0 for tract no 63 with population 787145.9538642855\n",
      "I am working on tract number 80 of 3168 tracts\n",
      "loop no 9.0 for tract no 89 with population 782225.8530175879\n",
      "tract,wide, thin angles are  90 86.4379 93.5621\n",
      "I am working on tract number 100 of 3168 tracts\n",
      "loop no 9.0 for tract no 107 with population 783451.4114488331\n",
      "tract,wide, thin angles are  108 114.9897 65.0103\n",
      "loop no 9.0 for tract no 108 with population 782425.8692095713\n",
      "loop no 9.0 for tract no 109 with population 782279.7293262222\n",
      "loop no 9.0 for tract no 114 with population 769237.7021392282\n",
      "loop no 10.0 for tract no 114 with population 780670.5524013784\n",
      "loop no 9.0 for tract no 116 with population 777173.531516627\n",
      "loop no 10.0 for tract no 116 with population 778593.6275103026\n",
      "loop no 11.0 for tract no 116 with population 783907.5415199936\n",
      "tract,wide, thin angles are  117 139.8563 40.1437\n",
      "loop no 9.0 for tract no 118 with population 783845.5033759768\n",
      "loop no 9.0 for tract no 119 with population 784593.5820767899\n",
      "I am working on tract number 120 of 3168 tracts\n",
      "loop no 9.0 for tract no 138 with population 777006.6155572105\n",
      "loop no 10.0 for tract no 138 with population 783133.1532299544\n",
      "I am working on tract number 140 of 3168 tracts\n",
      "I am working on tract number 160 of 3168 tracts\n",
      "loop no 9.0 for tract no 176 with population 774176.8186674\n",
      "loop no 10.0 for tract no 176 with population 780901.4668941054\n",
      "loop no 9.0 for tract no 179 with population 773800.4539669277\n",
      "loop no 10.0 for tract no 179 with population 783176.3490176549\n",
      "I am working on tract number 180 of 3168 tracts\n",
      "tract,wide, thin angles are  180 140.9921 39.0079\n",
      "loop no 9.0 for tract no 180 with population 771080.7003611124\n",
      "loop no 10.0 for tract no 180 with population 781867.5565594684\n",
      "loop no 9.0 for tract no 190 with population 518802.6680033271\n",
      "loop no 10.0 for tract no 190 with population 691721.8173476403\n",
      "loop no 11.0 for tract no 190 with population 756103.2975101648\n",
      "loop no 12.0 for tract no 190 with population 747339.609929272\n",
      "loop no 13.0 for tract no 190 with population 766980.4748796417\n",
      "loop no 14.0 for tract no 190 with population 775853.5522117817\n",
      "loop no 15.0 for tract no 190 with population 780863.4747101662\n",
      "I am working on tract number 200 of 3168 tracts\n",
      "loop no 9.0 for tract no 203 with population 757930.903917019\n",
      "loop no 10.0 for tract no 203 with population 778061.9790234141\n",
      "loop no 11.0 for tract no 203 with population 783213.9243605171\n",
      "loop no 9.0 for tract no 205 with population 734278.0986828875\n",
      "loop no 10.0 for tract no 205 with population 767884.1888934012\n",
      "loop no 11.0 for tract no 205 with population 780323.4687031002\n",
      "tract,wide, thin angles are  207 135.4508 44.5492\n",
      "loop no 9.0 for tract no 207 with population 776553.9372972439\n",
      "loop no 10.0 for tract no 207 with population 783343.7428377427\n",
      "loop no 9.0 for tract no 212 with population 785882.155796984\n",
      "tract,wide, thin angles are  216 138.0047 41.9953\n",
      "I am working on tract number 220 of 3168 tracts\n",
      "tract,wide, thin angles are  234 88.2954 91.7046\n",
      "loop no 9.0 for tract no 234 with population 797366.1199038039\n",
      "loop no 10.0 for tract no 234 with population 795980.091388135\n",
      "loop no 11.0 for tract no 234 with population 795810.1493138021\n",
      "loop no 12.0 for tract no 234 with population 795757.3284998266\n",
      "loop no 13.0 for tract no 234 with population 795737.5111890385\n",
      "loop no 14.0 for tract no 234 with population 795729.0365681185\n",
      "loop no 15.0 for tract no 234 with population 795725.0123291628\n",
      "loop no 16.0 for tract no 234 with population 795722.8358499941\n",
      "loop no 17.0 for tract no 234 with population 795721.529958438\n",
      "loop no 18.0 for tract no 234 with population 795720.746422264\n",
      "loop no 19.0 for tract no 234 with population 795720.2533855763\n",
      "loop no 20.0 for tract no 234 with population 795719.957563391\n",
      "loop no 21.0 for tract no 234 with population 795719.7800699995\n",
      "loop no 22.0 for tract no 234 with population 795719.6735739701\n",
      "loop no 23.0 for tract no 234 with population 795719.6096763229\n",
      "loop no 24.0 for tract no 234 with population 795719.5713377317\n",
      "loop no 25.0 for tract no 234 with population 795719.548334587\n",
      "loop no 26.0 for tract no 234 with population 795719.5345326841\n",
      "loop no 27.0 for tract no 234 with population 795719.5262515598\n",
      "loop no 28.0 for tract no 234 with population 795719.5212828668\n",
      "loop no 29.0 for tract no 234 with population 795719.5183016588\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.40348 1.00573 205747.1711 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.70174 1.00573 196657.5265 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.98458 1.00573 196657.2948 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.20257 1.00573 196657.5258 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 234 with population 795719.5165129498\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.8468 1.00573 205747.1711 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.70174 1.00573 196657.5255 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.98458 1.00573 196657.2948 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.20257 1.00573 196657.5251 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 234, giving up w pop 795719.5165129498\n",
      "I am working on tract number 240 of 3168 tracts\n",
      "I am working on tract number 260 of 3168 tracts\n",
      "loop no 9.0 for tract no 265 with population 782675.0800257294\n",
      "loop no 9.0 for tract no 267 with population 782196.9408246668\n",
      "loop no 9.0 for tract no 268 with population 783262.6075540199\n",
      "loop no 9.0 for tract no 277 with population 806787.2663560761\n",
      "loop no 10.0 for tract no 277 with population 792039.4311973203\n",
      "I am working on tract number 280 of 3168 tracts\n",
      "loop no 9.0 for tract no 294 with population 782207.7920498567\n",
      "loop no 9.0 for tract no 296 with population 728333.6477799588\n",
      "loop no 10.0 for tract no 296 with population 757963.9329014256\n",
      "loop no 11.0 for tract no 296 with population 772514.3456543123\n",
      "loop no 12.0 for tract no 296 with population 780696.00922122\n",
      "I am working on tract number 300 of 3168 tracts\n",
      "tract,wide, thin angles are  315 142.153 37.847\n",
      "I am working on tract number 320 of 3168 tracts\n",
      "loop no 9.0 for tract no 335 with population 772459.4873910052\n",
      "loop no 10.0 for tract no 335 with population 783338.2573892194\n",
      "loop no 9.0 for tract no 336 with population 782121.7017033957\n",
      "loop no 9.0 for tract no 337 with population 770665.5755385258\n",
      "loop no 10.0 for tract no 337 with population 782598.553928968\n",
      "I am working on tract number 340 of 3168 tracts\n",
      "tract,wide, thin angles are  342 131.1618 48.8382\n",
      "loop no 9.0 for tract no 349 with population 744273.9390127901\n",
      "loop no 10.0 for tract no 349 with population 774653.7778158027\n",
      "loop no 11.0 for tract no 349 with population 779816.7130189977\n",
      "tract,wide, thin angles are  351 143.0065 36.9935\n",
      "loop no 9.0 for tract no 357 with population 783035.5472961986\n",
      "loop no 9.0 for tract no 359 with population 754500.1752709742\n",
      "loop no 10.0 for tract no 359 with population 794849.8099991975\n",
      "loop no 11.0 for tract no 359 with population 787032.1636047149\n",
      "I am working on tract number 360 of 3168 tracts\n",
      "tract,wide, thin angles are  360 143.6891 36.3109\n",
      "loop no 9.0 for tract no 360 with population 784678.417526003\n",
      "loop no 9.0 for tract no 361 with population 778781.4632442592\n",
      "loop no 9.0 for tract no 362 with population 677292.5078195223\n",
      "loop no 10.0 for tract no 362 with population 801833.2338907155\n",
      "loop no 11.0 for tract no 362 with population 766016.7158567592\n",
      "loop no 12.0 for tract no 362 with population 778495.4493425856\n",
      "loop no 13.0 for tract no 362 with population 782918.9006666063\n",
      "loop no 9.0 for tract no 363 with population 996178.7107383491\n",
      "loop no 10.0 for tract no 363 with population 861373.2794834322\n",
      "loop no 11.0 for tract no 363 with population 812356.4506739875\n",
      "loop no 12.0 for tract no 363 with population 800653.4453061811\n",
      "loop no 13.0 for tract no 363 with population 793680.9487782773\n",
      "loop no 9.0 for tract no 365 with population 608202.7428706699\n",
      "loop no 10.0 for tract no 365 with population 709874.6185369766\n",
      "loop no 11.0 for tract no 365 with population 764741.4964209327\n",
      "loop no 12.0 for tract no 365 with population 773911.8508141697\n",
      "loop no 13.0 for tract no 365 with population 780544.853442735\n",
      "loop no 9.0 for tract no 366 with population 506441.43762104557\n",
      "loop no 10.0 for tract no 366 with population 618197.1799046096\n",
      "loop no 11.0 for tract no 366 with population 704948.4646253492\n",
      "loop no 12.0 for tract no 366 with population 738366.5929302516\n",
      "loop no 13.0 for tract no 366 with population 773989.6086721685\n",
      "loop no 14.0 for tract no 366 with population 778349.0940522404\n",
      "loop no 15.0 for tract no 366 with population 781692.4156453924\n",
      "loop no 9.0 for tract no 367 with population 787375.0318471323\n",
      "tract,wide, thin angles are  369 128.0137 51.9863\n",
      "loop no 9.0 for tract no 369 with population 876264.3555976523\n",
      "loop no 10.0 for tract no 369 with population 786486.2241097672\n",
      "loop no 9.0 for tract no 370 with population 777668.632881082\n",
      "loop no 10.0 for tract no 370 with population 785769.8496783223\n",
      "loop no 9.0 for tract no 372 with population 773193.8779572247\n",
      "loop no 10.0 for tract no 372 with population 781732.6472471986\n",
      "loop no 9.0 for tract no 373 with population 782662.8903800139\n",
      "loop no 9.0 for tract no 374 with population 819326.4754285649\n",
      "loop no 10.0 for tract no 374 with population 801418.075789886\n",
      "loop no 11.0 for tract no 374 with population 786385.8781138535\n",
      "loop no 9.0 for tract no 375 with population 775700.2483939514\n",
      "loop no 10.0 for tract no 375 with population 783959.0767196574\n",
      "loop no 9.0 for tract no 376 with population 783290.0205155554\n",
      "loop no 9.0 for tract no 377 with population 780202.8675855566\n",
      "tract,wide, thin angles are  378 133.6377 46.3623\n",
      "loop no 9.0 for tract no 378 with population 659087.1729080393\n",
      "loop no 10.0 for tract no 378 with population 750219.9227433647\n",
      "loop no 11.0 for tract no 378 with population 775955.2963582121\n",
      "loop no 12.0 for tract no 378 with population 780849.0740665055\n",
      "loop no 9.0 for tract no 379 with population 717240.61460894\n",
      "loop no 10.0 for tract no 379 with population 767170.1923828913\n",
      "loop no 11.0 for tract no 379 with population 778007.5532653227\n",
      "loop no 12.0 for tract no 379 with population 781989.7747477016\n",
      "I am working on tract number 380 of 3168 tracts\n",
      "loop no 9.0 for tract no 381 with population 767188.7612116615\n",
      "loop no 10.0 for tract no 381 with population 779066.97840447\n",
      "loop no 9.0 for tract no 382 with population 819575.545279958\n",
      "loop no 10.0 for tract no 382 with population 777353.7323175776\n",
      "loop no 11.0 for tract no 382 with population 781001.4038577286\n",
      "loop no 9.0 for tract no 384 with population 756182.7428058125\n",
      "loop no 10.0 for tract no 384 with population 781250.2488101339\n",
      "loop no 9.0 for tract no 385 with population 784032.0751022943\n",
      "loop no 9.0 for tract no 386 with population 786868.701063493\n",
      "tract,wide, thin angles are  387 143.7255 36.2745\n",
      "loop no 9.0 for tract no 388 with population 705513.4328716347\n",
      "loop no 10.0 for tract no 388 with population 755621.6708117743\n",
      "loop no 11.0 for tract no 388 with population 776571.4281314021\n",
      "loop no 12.0 for tract no 388 with population 783459.0697412167\n",
      "loop no 9.0 for tract no 389 with population 778870.7832954201\n",
      "loop no 9.0 for tract no 390 with population 787244.6787964082\n",
      "loop no 9.0 for tract no 392 with population 767668.8449266519\n",
      "loop no 10.0 for tract no 392 with population 774934.4159289801\n",
      "loop no 11.0 for tract no 392 with population 775033.5606276635\n",
      "loop no 12.0 for tract no 392 with population 781641.9024765942\n",
      "loop no 9.0 for tract no 393 with population 820442.6596444002\n",
      "loop no 10.0 for tract no 393 with population 782314.317617815\n",
      "loop no 9.0 for tract no 394 with population 903874.875844734\n",
      "loop no 10.0 for tract no 394 with population 816504.8354437974\n",
      "loop no 11.0 for tract no 394 with population 800211.7323823588\n",
      "loop no 12.0 for tract no 394 with population 793238.8203394476\n",
      "loop no 9.0 for tract no 395 with population 761636.7830385605\n",
      "loop no 10.0 for tract no 395 with population 783934.8511164272\n",
      "tract,wide, thin angles are  396 108.0468 71.9532\n",
      "loop no 9.0 for tract no 396 with population 778423.9742409252\n",
      "loop no 10.0 for tract no 396 with population 787554.1482273852\n",
      "loop no 9.0 for tract no 397 with population 777885.3268036658\n",
      "loop no 10.0 for tract no 397 with population 779709.5177768646\n",
      "loop no 9.0 for tract no 398 with population 819018.7893398332\n",
      "loop no 10.0 for tract no 398 with population 786329.3823477014\n",
      "I am working on tract number 400 of 3168 tracts\n",
      "loop no 9.0 for tract no 400 with population 907562.6248064839\n",
      "loop no 10.0 for tract no 400 with population 859495.681659202\n",
      "loop no 11.0 for tract no 400 with population 808348.7002284825\n",
      "loop no 12.0 for tract no 400 with population 792104.3406061864\n",
      "loop no 9.0 for tract no 401 with population 761433.4035904006\n",
      "loop no 10.0 for tract no 401 with population 788044.61135001\n",
      "loop no 9.0 for tract no 402 with population 573344.0622094504\n",
      "loop no 10.0 for tract no 402 with population 654246.7627248525\n",
      "loop no 11.0 for tract no 402 with population 746368.5518446703\n",
      "loop no 12.0 for tract no 402 with population 775476.2466867544\n",
      "loop no 13.0 for tract no 402 with population 778100.8940554545\n",
      "loop no 14.0 for tract no 402 with population 783433.1048037958\n",
      "loop no 9.0 for tract no 404 with population 779694.0007848333\n",
      "tract,wide, thin angles are  405 130.2587 49.7413\n",
      "loop no 9.0 for tract no 405 with population 916723.0696256796\n",
      "loop no 10.0 for tract no 405 with population 804380.5630435997\n",
      "loop no 11.0 for tract no 405 with population 791981.3927846076\n",
      "loop no 9.0 for tract no 407 with population 710240.3393054768\n",
      "loop no 10.0 for tract no 407 with population 742256.4708985846\n",
      "loop no 11.0 for tract no 407 with population 753599.0859028955\n",
      "loop no 12.0 for tract no 407 with population 772691.0746013308\n",
      "loop no 13.0 for tract no 407 with population 778635.5189059519\n",
      "loop no 14.0 for tract no 407 with population 782242.3008732729\n",
      "tract,wide, thin angles are  414 139.9055 40.0945\n",
      "loop no 9.0 for tract no 414 with population 917301.7038359222\n",
      "loop no 10.0 for tract no 414 with population 829737.7373597109\n",
      "loop no 11.0 for tract no 414 with population 800517.8551390708\n",
      "loop no 12.0 for tract no 414 with population 794676.2370896355\n",
      "loop no 13.0 for tract no 414 with population 789952.2140792354\n",
      "loop no 9.0 for tract no 416 with population 722227.9605431408\n",
      "loop no 10.0 for tract no 416 with population 790874.1845283448\n",
      "loop no 9.0 for tract no 417 with population 925852.8039797965\n",
      "loop no 10.0 for tract no 417 with population 859563.6537380628\n",
      "loop no 11.0 for tract no 417 with population 811197.6733330537\n",
      "loop no 12.0 for tract no 417 with population 792961.1240527409\n",
      "loop no 9.0 for tract no 418 with population 761665.3194302503\n",
      "loop no 10.0 for tract no 418 with population 777505.858530678\n",
      "loop no 11.0 for tract no 418 with population 783976.9866034212\n",
      "loop no 9.0 for tract no 419 with population 764149.3797613145\n",
      "loop no 10.0 for tract no 419 with population 780729.5689035822\n",
      "I am working on tract number 420 of 3168 tracts\n",
      "loop no 9.0 for tract no 420 with population 628279.5893884953\n",
      "loop no 10.0 for tract no 420 with population 664589.3542176224\n",
      "loop no 11.0 for tract no 420 with population 745180.6797617506\n",
      "loop no 12.0 for tract no 420 with population 810100.2815322207\n",
      "loop no 13.0 for tract no 420 with population 801592.2491861002\n",
      "loop no 14.0 for tract no 420 with population 790803.8939680284\n",
      "loop no 9.0 for tract no 421 with population 796815.9171820644\n",
      "loop no 10.0 for tract no 421 with population 789037.2433063928\n",
      "tract,wide, thin angles are  423 100.6649 79.3351\n",
      "loop no 9.0 for tract no 424 with population 775663.0940300531\n",
      "loop no 10.0 for tract no 424 with population 778697.504031671\n",
      "loop no 11.0 for tract no 424 with population 791051.8245488587\n",
      "loop no 9.0 for tract no 425 with population 755879.7996568258\n",
      "loop no 10.0 for tract no 425 with population 772919.0875026505\n",
      "loop no 11.0 for tract no 425 with population 783645.0421511864\n",
      "loop no 9.0 for tract no 426 with population 782607.7805940777\n",
      "loop no 9.0 for tract no 427 with population 717008.6713980288\n",
      "loop no 10.0 for tract no 427 with population 913118.429732807\n",
      "loop no 11.0 for tract no 427 with population 790282.8794207745\n",
      "loop no 9.0 for tract no 429 with population 781920.7622445538\n",
      "tract,wide, thin angles are  432 120.0111 59.9889\n",
      "loop no 9.0 for tract no 432 with population 769522.7040719935\n",
      "loop no 10.0 for tract no 432 with population 790077.4665189759\n",
      "loop no 9.0 for tract no 434 with population 761959.7845807095\n",
      "loop no 10.0 for tract no 434 with population 774519.2265402928\n",
      "loop no 11.0 for tract no 434 with population 786459.3980843077\n",
      "loop no 9.0 for tract no 436 with population 610255.1866379591\n",
      "loop no 10.0 for tract no 436 with population 758391.6716636338\n",
      "loop no 11.0 for tract no 436 with population 767905.2730252267\n",
      "loop no 12.0 for tract no 436 with population 784853.7671232208\n",
      "loop no 9.0 for tract no 437 with population 769739.773392253\n",
      "loop no 10.0 for tract no 437 with population 779014.890633767\n",
      "loop no 9.0 for tract no 438 with population 700424.913902024\n",
      "loop no 10.0 for tract no 438 with population 756797.7519364345\n",
      "loop no 11.0 for tract no 438 with population 765320.9602464491\n",
      "loop no 12.0 for tract no 438 with population 784948.573193203\n",
      "loop no 9.0 for tract no 439 with population 750406.6132431533\n",
      "loop no 10.0 for tract no 439 with population 791837.2208481164\n",
      "I am working on tract number 440 of 3168 tracts\n",
      "loop no 9.0 for tract no 440 with population 727979.9370446125\n",
      "loop no 10.0 for tract no 440 with population 767174.8051267774\n",
      "loop no 11.0 for tract no 440 with population 782281.6599545866\n",
      "tract,wide, thin angles are  441 140.1061 39.8939\n",
      "loop no 9.0 for tract no 441 with population 782053.8569725072\n",
      "we have 2 non-opposing shorted wedges for tract no 442\n",
      "tract,wide, thin angles are  450 143.7963 36.2037\n",
      "loop no 9.0 for tract no 450 with population 780384.8382670307\n",
      "loop no 9.0 for tract no 454 with population 743037.8347646424\n",
      "loop no 10.0 for tract no 454 with population 771963.4800643477\n",
      "loop no 11.0 for tract no 454 with population 781688.4945492686\n",
      "loop no 9.0 for tract no 455 with population 775565.4615194227\n",
      "loop no 10.0 for tract no 455 with population 782890.4154070964\n",
      "loop no 9.0 for tract no 456 with population 777834.4830517791\n",
      "loop no 10.0 for tract no 456 with population 787107.852078553\n",
      "loop no 9.0 for tract no 457 with population 785699.4240023987\n",
      "loop no 9.0 for tract no 458 with population 755284.8511021191\n",
      "loop no 10.0 for tract no 458 with population 787152.5365857868\n",
      "I am working on tract number 460 of 3168 tracts\n",
      "loop no 9.0 for tract no 474 with population 788459.5064568098\n",
      "loop no 9.0 for tract no 476 with population 780486.7219889015\n",
      "loop no 9.0 for tract no 478 with population 783650.7965492264\n",
      "I am working on tract number 480 of 3168 tracts\n",
      "tract,wide, thin angles are  486 94.5863 85.4137\n",
      "loop no 9.0 for tract no 486 with population 784101.0405567302\n",
      "loop no 9.0 for tract no 490 with population 780548.722615588\n",
      "loop no 9.0 for tract no 493 with population 782684.890672789\n",
      "loop no 9.0 for tract no 494 with population 782924.695401152\n",
      "tract,wide, thin angles are  495 131.9699 48.0301\n",
      "loop no 9.0 for tract no 495 with population 799759.5305954304\n",
      "loop no 10.0 for tract no 495 with population 790954.2950915315\n",
      "loop no 9.0 for tract no 497 with population 828482.8847451393\n",
      "loop no 10.0 for tract no 497 with population 806149.9466910701\n",
      "loop no 11.0 for tract no 497 with population 795795.7444087381\n",
      "loop no 12.0 for tract no 497 with population 786969.430565306\n",
      "I am working on tract number 500 of 3168 tracts\n",
      "loop no 9.0 for tract no 501 with population 782836.9850887405\n",
      "loop no 9.0 for tract no 502 with population 778393.5363430609\n",
      "loop no 10.0 for tract no 502 with population 783967.5593567649\n",
      "loop no 9.0 for tract no 506 with population 784496.939724139\n",
      "loop no 9.0 for tract no 507 with population 784232.3737618193\n",
      "loop no 9.0 for tract no 509 with population 780372.9764255193\n",
      "loop no 9.0 for tract no 515 with population 746941.9962618845\n",
      "loop no 10.0 for tract no 515 with population 764725.3863930881\n",
      "loop no 11.0 for tract no 515 with population 779314.9720206108\n",
      "loop no 9.0 for tract no 518 with population 777748.9489593089\n",
      "loop no 10.0 for tract no 518 with population 784427.2552677251\n",
      "I am working on tract number 520 of 3168 tracts\n",
      "loop no 9.0 for tract no 523 with population 785973.8816954895\n",
      "loop no 9.0 for tract no 524 with population 783131.3665495941\n",
      "loop no 9.0 for tract no 527 with population 821575.699034604\n",
      "loop no 10.0 for tract no 527 with population 825307.1538230648\n",
      "loop no 11.0 for tract no 527 with population 825803.1210777698\n",
      "loop no 12.0 for tract no 527 with population 825939.5348989324\n",
      "loop no 13.0 for tract no 527 with population 825990.1835271806\n",
      "loop no 14.0 for tract no 527 with population 826011.7902638579\n",
      "loop no 15.0 for tract no 527 with population 826022.0423776037\n",
      "loop no 16.0 for tract no 527 with population 826027.5855489902\n",
      "loop no 17.0 for tract no 527 with population 826030.9110449625\n",
      "loop no 18.0 for tract no 527 with population 826032.9062088898\n",
      "loop no 19.0 for tract no 527 with population 826033.7603556188\n",
      "loop no 20.0 for tract no 527 with population 826034.2728449954\n",
      "loop no 21.0 for tract no 527 with population 826034.5803394811\n",
      "loop no 22.0 for tract no 527 with population 826034.7648364906\n",
      "loop no 23.0 for tract no 527 with population 826034.8755348015\n",
      "loop no 24.0 for tract no 527 with population 826034.941953823\n",
      "loop no 25.0 for tract no 527 with population 826034.9818052633\n",
      "loop no 26.0 for tract no 527 with population 826035.0057161225\n",
      "loop no 27.0 for tract no 527 with population 826035.0200626426\n",
      "loop no 28.0 for tract no 527 with population 826035.0286705587\n",
      "loop no 29.0 for tract no 527 with population 826035.0338353021\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.27284 0.38103 236063.4988 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.31485 0.38103 196658.3217 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.89503 0.38103 196658.3183 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.23535 0.38103 196654.8951 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 527 with population 826035.0369341581\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.13984 0.38103 236063.4988 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.31485 0.38103 196658.3225 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.89503 0.38103 196658.3205 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.23535 0.38103 196654.8951 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 527, giving up w pop 826035.0369341581\n",
      "loop no 9.0 for tract no 528 with population 798932.6251805121\n",
      "loop no 10.0 for tract no 528 with population 800434.2967136318\n",
      "loop no 11.0 for tract no 528 with population 800603.4646512973\n",
      "loop no 12.0 for tract no 528 with population 800644.0865777668\n",
      "loop no 13.0 for tract no 528 with population 800659.3185779742\n",
      "loop no 14.0 for tract no 528 with population 800665.8314031905\n",
      "loop no 15.0 for tract no 528 with population 800668.9239343094\n",
      "loop no 16.0 for tract no 528 with population 800670.5964833561\n",
      "loop no 17.0 for tract no 528 with population 800671.6000102148\n",
      "loop no 18.0 for tract no 528 with population 800672.2021252133\n",
      "loop no 19.0 for tract no 528 with population 800672.4240977219\n",
      "loop no 20.0 for tract no 528 with population 800672.5572812704\n",
      "loop no 21.0 for tract no 528 with population 800672.6371917775\n",
      "loop no 22.0 for tract no 528 with population 800672.6851381725\n",
      "loop no 23.0 for tract no 528 with population 800672.7139060979\n",
      "loop no 24.0 for tract no 528 with population 800672.7311668684\n",
      "loop no 25.0 for tract no 528 with population 800672.7415233222\n",
      "loop no 26.0 for tract no 528 with population 800672.747737182\n",
      "loop no 27.0 for tract no 528 with population 800672.7514655252\n",
      "loop no 28.0 for tract no 528 with population 800672.7537025323\n",
      "loop no 29.0 for tract no 528 with population 800672.7549462256\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.29193 0.38103 210700.7053 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.30289 0.38103 196657.8141 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 1.01155 0.38103 196656.422 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.17945 0.38103 196657.8136 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 528 with population 800672.7555191844\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.15893 0.38103 210700.7053 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.30289 0.38103 196657.8142 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 1.01155 0.38103 196656.422 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.17945 0.38103 196657.8141 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 528, giving up w pop 800672.7555191844\n",
      "loop no 9.0 for tract no 536 with population 781812.6836165546\n",
      "loop no 9.0 for tract no 537 with population 793249.8287101039\n",
      "loop no 9.0 for tract no 538 with population 795037.4474973581\n",
      "loop no 10.0 for tract no 538 with population 788860.5418832311\n",
      "loop no 9.0 for tract no 539 with population 762875.1348288032\n",
      "loop no 10.0 for tract no 539 with population 780118.7379031096\n",
      "I am working on tract number 540 of 3168 tracts\n",
      "loop no 9.0 for tract no 547 with population 741518.885491936\n",
      "loop no 10.0 for tract no 547 with population 787660.3896659089\n",
      "tract,wide, thin angles are  549 85.3511 94.6489\n",
      "loop no 9.0 for tract no 549 with population 771601.5668025205\n",
      "loop no 10.0 for tract no 549 with population 781470.1319940182\n",
      "I am working on tract number 560 of 3168 tracts\n",
      "I am working on tract number 580 of 3168 tracts\n",
      "loop no 9.0 for tract no 581 with population 783296.4635494904\n",
      "tract,wide, thin angles are  585 141.717 38.283\n",
      "we have 2 non-opposing shorted wedges for tract no 593\n",
      "tract,wide, thin angles are  594 88.8159 91.1841\n",
      "loop no 9.0 for tract no 594 with population 822236.8881428969\n",
      "loop no 10.0 for tract no 594 with population 787978.8601285424\n",
      "I am working on tract number 600 of 3168 tracts\n",
      "loop no 9.0 for tract no 609 with population 784217.541713682\n",
      "loop no 9.0 for tract no 610 with population 810411.6262949859\n",
      "loop no 10.0 for tract no 610 with population 798880.7334538582\n",
      "loop no 11.0 for tract no 610 with population 790205.9145579988\n",
      "we have 2 non-opposing shorted wedges for tract no 611\n",
      "tract,wide, thin angles are  612 143.8208 36.1792\n",
      "loop no 9.0 for tract no 612 with population 829190.273651206\n",
      "loop no 10.0 for tract no 612 with population 798286.0215968137\n",
      "loop no 11.0 for tract no 612 with population 790498.2308218549\n",
      "loop no 9.0 for tract no 617 with population 795938.746834221\n",
      "loop no 10.0 for tract no 617 with population 784297.9594729927\n",
      "loop no 9.0 for tract no 618 with population 768274.1654050446\n",
      "loop no 10.0 for tract no 618 with population 779764.8294688575\n",
      "I am working on tract number 620 of 3168 tracts\n",
      "loop no 9.0 for tract no 620 with population 780129.488129042\n",
      "loop no 9.0 for tract no 629 with population 762789.3218703974\n",
      "loop no 10.0 for tract no 629 with population 779969.4164830243\n",
      "tract,wide, thin angles are  630 140.1819 39.8181\n",
      "loop no 9.0 for tract no 630 with population 736717.36275571\n",
      "loop no 10.0 for tract no 630 with population 771526.477388781\n",
      "loop no 11.0 for tract no 630 with population 780943.448025834\n",
      "loop no 9.0 for tract no 632 with population 940637.4643757779\n",
      "loop no 10.0 for tract no 632 with population 858347.8057914136\n",
      "loop no 11.0 for tract no 632 with population 815668.9786238992\n",
      "loop no 12.0 for tract no 632 with population 797537.8827696536\n",
      "loop no 13.0 for tract no 632 with population 793614.0275043338\n",
      "loop no 9.0 for tract no 634 with population 787441.0688081518\n",
      "loop no 9.0 for tract no 635 with population 782253.9903729463\n",
      "tract,wide, thin angles are  639 135.24 44.76\n",
      "loop no 9.0 for tract no 639 with population 748435.5558825957\n",
      "loop no 10.0 for tract no 639 with population 771178.2078299543\n",
      "loop no 11.0 for tract no 639 with population 781447.9396163933\n",
      "I am working on tract number 640 of 3168 tracts\n",
      "loop no 9.0 for tract no 641 with population 770969.4040417079\n",
      "loop no 10.0 for tract no 641 with population 780076.5660572924\n",
      "loop no 9.0 for tract no 642 with population 727059.8296085274\n",
      "loop no 10.0 for tract no 642 with population 773850.0818697196\n",
      "loop no 11.0 for tract no 642 with population 784156.4752432364\n",
      "loop no 9.0 for tract no 643 with population 770651.625202182\n",
      "loop no 10.0 for tract no 643 with population 783873.0263231805\n",
      "loop no 9.0 for tract no 645 with population 784960.5602012195\n",
      "loop no 9.0 for tract no 647 with population 542692.4298080126\n",
      "loop no 10.0 for tract no 647 with population 673601.053870683\n",
      "loop no 11.0 for tract no 647 with population 729009.7156952802\n",
      "loop no 12.0 for tract no 647 with population 762487.8950136566\n",
      "loop no 13.0 for tract no 647 with population 773708.800758692\n",
      "loop no 14.0 for tract no 647 with population 780055.5719356152\n",
      "tract,wide, thin angles are  648 132.6506 47.3494\n",
      "we have 2 non-opposing shorted wedges for tract no 652\n",
      "we have 2 non-opposing shorted wedges for tract no 653\n",
      "loop no 9.0 for tract no 654 with population 793161.6420748625\n",
      "we have 2 non-opposing shorted wedges for tract no 655\n",
      "tract,wide, thin angles are  657 108.591 71.409\n",
      "loop no 9.0 for tract no 657 with population 791984.4995837878\n",
      "I am working on tract number 660 of 3168 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 664\n",
      "tract,wide, thin angles are  666 134.6093 45.3907\n",
      "loop no 9.0 for tract no 667 with population 788311.2488688136\n",
      "loop no 9.0 for tract no 668 with population 769358.3379377387\n",
      "loop no 10.0 for tract no 668 with population 784533.6176976964\n",
      "loop no 9.0 for tract no 669 with population 780249.753700854\n",
      "loop no 9.0 for tract no 670 with population 753548.8276838904\n",
      "loop no 10.0 for tract no 670 with population 777506.8329258665\n",
      "loop no 11.0 for tract no 670 with population 785533.9487727146\n",
      "loop no 9.0 for tract no 671 with population 755645.5122581266\n",
      "loop no 10.0 for tract no 671 with population 778569.4160888904\n",
      "loop no 11.0 for tract no 671 with population 785026.829930253\n",
      "we have 2 non-opposing shorted wedges for tract no 672\n",
      "loop no 9.0 for tract no 673 with population 758658.6579951826\n",
      "loop no 10.0 for tract no 673 with population 777971.8629558411\n",
      "loop no 11.0 for tract no 673 with population 784091.6120039946\n",
      "loop no 9.0 for tract no 674 with population 793949.4229524251\n",
      "I am working on tract number 680 of 3168 tracts\n",
      "tract,wide, thin angles are  684 133.1527 46.8473\n",
      "loop no 9.0 for tract no 685 with population 837122.1557984722\n",
      "loop no 10.0 for tract no 685 with population 801969.4028773415\n",
      "loop no 11.0 for tract no 685 with population 791902.8946662026\n",
      "we have 2 non-opposing shorted wedges for tract no 689\n",
      "loop no 9.0 for tract no 692 with population 760396.2761353697\n",
      "loop no 10.0 for tract no 692 with population 771196.2084265029\n",
      "loop no 11.0 for tract no 692 with population 781119.9161439008\n",
      "tract,wide, thin angles are  693 90.2726 89.7274\n",
      "loop no 9.0 for tract no 694 with population 790586.9412560207\n",
      "loop no 9.0 for tract no 698 with population 752783.1708029427\n",
      "loop no 10.0 for tract no 698 with population 780281.4582765836\n",
      "loop no 9.0 for tract no 699 with population 767794.7178308034\n",
      "loop no 10.0 for tract no 699 with population 782871.5993773642\n",
      "I am working on tract number 700 of 3168 tracts\n",
      "loop no 9.0 for tract no 701 with population 787591.583749833\n",
      "tract,wide, thin angles are  702 141.9534 38.0466\n",
      "loop no 9.0 for tract no 702 with population 784825.7968525373\n",
      "loop no 9.0 for tract no 703 with population 770933.494291175\n",
      "loop no 10.0 for tract no 703 with population 781525.093332249\n",
      "loop no 9.0 for tract no 705 with population 784269.5567803278\n",
      "loop no 9.0 for tract no 706 with population 970812.5798363322\n",
      "loop no 10.0 for tract no 706 with population 865894.0834631375\n",
      "loop no 11.0 for tract no 706 with population 811641.4285207499\n",
      "loop no 12.0 for tract no 706 with population 797114.7432188024\n",
      "loop no 13.0 for tract no 706 with population 791819.803554439\n",
      "I am working on tract number 720 of 3168 tracts\n",
      "loop no 9.0 for tract no 724 with population 772220.1304492326\n",
      "loop no 10.0 for tract no 724 with population 781463.8228337533\n",
      "loop no 9.0 for tract no 726 with population 817252.7938596659\n",
      "loop no 10.0 for tract no 726 with population 786747.3469568603\n",
      "loop no 9.0 for tract no 727 with population 707490.5106226467\n",
      "loop no 10.0 for tract no 727 with population 749010.5269198756\n",
      "loop no 11.0 for tract no 727 with population 770443.763619484\n",
      "loop no 12.0 for tract no 727 with population 779090.8828202842\n",
      "loop no 9.0 for tract no 731 with population 736226.8684342159\n",
      "loop no 10.0 for tract no 731 with population 684875.880925525\n",
      "loop no 11.0 for tract no 731 with population 741930.9202215143\n",
      "loop no 12.0 for tract no 731 with population 761470.3713127002\n",
      "loop no 13.0 for tract no 731 with population 775708.672772645\n",
      "loop no 14.0 for tract no 731 with population 781315.7746482878\n",
      "loop no 9.0 for tract no 733 with population 770263.7722941106\n",
      "loop no 10.0 for tract no 733 with population 776312.4203583043\n",
      "loop no 11.0 for tract no 733 with population 782490.8435113803\n",
      "we have 2 non-opposing shorted wedges for tract no 734\n",
      "loop no 9.0 for tract no 735 with population 776192.840917137\n",
      "loop no 10.0 for tract no 735 with population 783942.6996671369\n",
      "we have 2 non-opposing shorted wedges for tract no 738\n",
      "loop no 9.0 for tract no 739 with population 793128.2052345589\n",
      "I am working on tract number 740 of 3168 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 740\n",
      "loop no 9.0 for tract no 741 with population 761719.6422143793\n",
      "loop no 10.0 for tract no 741 with population 777883.6419026698\n",
      "loop no 11.0 for tract no 741 with population 783214.4860069533\n",
      "loop no 9.0 for tract no 743 with population 678855.7118801326\n",
      "loop no 10.0 for tract no 743 with population 704057.0667459107\n",
      "loop no 11.0 for tract no 743 with population 740684.4541479922\n",
      "loop no 12.0 for tract no 743 with population 757317.0868848175\n",
      "loop no 13.0 for tract no 743 with population 773659.4339067348\n",
      "loop no 14.0 for tract no 743 with population 778398.7784849162\n",
      "loop no 15.0 for tract no 743 with population 781650.6865704396\n",
      "tract,wide, thin angles are  747 143.7576 36.2424\n",
      "loop no 9.0 for tract no 748 with population 779873.3640692707\n",
      "tract,wide, thin angles are  756 135.0882 44.9118\n",
      "I am working on tract number 760 of 3168 tracts\n",
      "loop no 9.0 for tract no 768 with population 810940.1788407961\n",
      "loop no 10.0 for tract no 768 with population 740278.6115652132\n",
      "loop no 11.0 for tract no 768 with population 803115.3637787714\n",
      "loop no 12.0 for tract no 768 with population 794772.0133737872\n",
      "loop no 13.0 for tract no 768 with population 790702.7041517003\n",
      "loop no 9.0 for tract no 771 with population 791337.39029755\n",
      "tract,wide, thin angles are  774 142.4943 37.5057\n",
      "loop no 9.0 for tract no 779 with population 794287.578346566\n",
      "I am working on tract number 780 of 3168 tracts\n",
      "tract,wide, thin angles are  783 131.5503 48.4497\n",
      "loop no 9.0 for tract no 783 with population 790012.6828194739\n",
      "loop no 9.0 for tract no 794 with population 792239.8937023378\n",
      "loop no 9.0 for tract no 795 with population 649556.9021380001\n",
      "loop no 10.0 for tract no 795 with population 736586.8305058905\n",
      "loop no 11.0 for tract no 795 with population 762968.9134520798\n",
      "loop no 12.0 for tract no 795 with population 772820.9933607546\n",
      "loop no 13.0 for tract no 795 with population 782533.1162014641\n",
      "loop no 9.0 for tract no 797 with population 770254.8705759238\n",
      "loop no 10.0 for tract no 797 with population 780834.6842056795\n",
      "loop no 9.0 for tract no 798 with population 773501.6474990736\n",
      "loop no 10.0 for tract no 798 with population 779879.2974019776\n",
      "loop no 9.0 for tract no 799 with population 658958.0071861283\n",
      "loop no 10.0 for tract no 799 with population 758817.5655056289\n",
      "loop no 11.0 for tract no 799 with population 796968.5161540075\n",
      "loop no 12.0 for tract no 799 with population 792927.9339476674\n",
      "I am working on tract number 800 of 3168 tracts\n",
      "tract,wide, thin angles are  801 135.8121 44.1879\n",
      "loop no 9.0 for tract no 801 with population 784955.1395630366\n",
      "loop no 9.0 for tract no 809 with population 784641.0555495444\n",
      "tract,wide, thin angles are  810 142.2518 37.7482\n",
      "loop no 9.0 for tract no 810 with population 782264.4182261999\n",
      "loop no 9.0 for tract no 811 with population 781636.4989993512\n",
      "loop no 9.0 for tract no 812 with population 751042.2924260449\n",
      "loop no 10.0 for tract no 812 with population 778886.9259832648\n",
      "loop no 9.0 for tract no 813 with population 442998.93132090353\n",
      "loop no 10.0 for tract no 813 with population 606703.3719106349\n",
      "loop no 11.0 for tract no 813 with population 685396.0682486312\n",
      "loop no 12.0 for tract no 813 with population 744806.0007263729\n",
      "loop no 13.0 for tract no 813 with population 764513.8936796288\n",
      "loop no 14.0 for tract no 813 with population 773890.4288722049\n",
      "loop no 15.0 for tract no 813 with population 778906.1587094337\n",
      "loop no 9.0 for tract no 814 with population 670025.7603740698\n",
      "loop no 10.0 for tract no 814 with population 761814.5818020552\n",
      "loop no 11.0 for tract no 814 with population 775164.5425679567\n",
      "loop no 12.0 for tract no 814 with population 783095.4970605109\n",
      "loop no 9.0 for tract no 815 with population 775733.4764511948\n",
      "loop no 10.0 for tract no 815 with population 780066.4794993909\n",
      "I am working on tract number 820 of 3168 tracts\n",
      "loop no 9.0 for tract no 836 with population 773341.6655615773\n",
      "loop no 10.0 for tract no 836 with population 781349.0747517543\n",
      "we have 2 non-opposing shorted wedges for tract no 837\n",
      "loop no 9.0 for tract no 838 with population 781039.5788568095\n",
      "loop no 9.0 for tract no 839 with population 794254.1535891389\n",
      "I am working on tract number 840 of 3168 tracts\n",
      "loop no 9.0 for tract no 841 with population 735093.3880582524\n",
      "loop no 10.0 for tract no 841 with population 807931.6721289466\n",
      "loop no 11.0 for tract no 841 with population 793531.6466394467\n",
      "loop no 9.0 for tract no 842 with population 788723.4642225464\n",
      "loop no 9.0 for tract no 843 with population 779068.1607428499\n",
      "loop no 9.0 for tract no 844 with population 780621.1459672014\n",
      "loop no 9.0 for tract no 845 with population 756730.5581299897\n",
      "loop no 10.0 for tract no 845 with population 773849.7053901174\n",
      "loop no 11.0 for tract no 845 with population 780915.6985899009\n",
      "tract,wide, thin angles are  846 90.4183 89.5817\n",
      "loop no 9.0 for tract no 846 with population 767591.1869390685\n",
      "loop no 10.0 for tract no 846 with population 777014.8654296295\n",
      "loop no 11.0 for tract no 846 with population 783077.9259032935\n",
      "tract,wide, thin angles are  855 133.9092 46.0908\n",
      "I am working on tract number 860 of 3168 tracts\n",
      "tract,wide, thin angles are  864 142.7711 37.2289\n",
      "loop no 9.0 for tract no 867 with population 704348.2849698763\n",
      "loop no 10.0 for tract no 867 with population 748390.2213060798\n",
      "loop no 11.0 for tract no 867 with population 712879.2475698751\n",
      "loop no 12.0 for tract no 867 with population 768709.8487015271\n",
      "loop no 13.0 for tract no 867 with population 777464.414585995\n",
      "loop no 14.0 for tract no 867 with population 781631.7742205401\n",
      "loop no 9.0 for tract no 869 with population 713672.2351704182\n",
      "loop no 10.0 for tract no 869 with population 747784.5183183351\n",
      "loop no 11.0 for tract no 869 with population 772377.9101267227\n",
      "loop no 12.0 for tract no 869 with population 780401.6836228943\n",
      "loop no 9.0 for tract no 870 with population 782689.8408891099\n",
      "I am working on tract number 880 of 3168 tracts\n",
      "loop no 9.0 for tract no 887 with population 766565.9528834156\n",
      "loop no 10.0 for tract no 887 with population 778325.2502882947\n",
      "loop no 11.0 for tract no 887 with population 782242.0419436508\n",
      "loop no 9.0 for tract no 888 with population 803093.3828838689\n",
      "loop no 10.0 for tract no 888 with population 807817.5837883649\n",
      "loop no 11.0 for tract no 888 with population 808948.1203647697\n",
      "loop no 12.0 for tract no 888 with population 809284.8645226241\n",
      "loop no 13.0 for tract no 888 with population 809411.8061362584\n",
      "loop no 14.0 for tract no 888 with population 809466.1149805147\n",
      "loop no 15.0 for tract no 888 with population 809491.9073098332\n",
      "loop no 16.0 for tract no 888 with population 809505.8579156926\n",
      "loop no 17.0 for tract no 888 with population 809514.2286761496\n",
      "loop no 18.0 for tract no 888 with population 809519.251277471\n",
      "loop no 19.0 for tract no 888 with population 809521.376127135\n",
      "loop no 20.0 for tract no 888 with population 809522.6510508932\n",
      "loop no 21.0 for tract no 888 with population 809523.4160120867\n",
      "loop no 22.0 for tract no 888 with population 809523.8749912563\n",
      "loop no 23.0 for tract no 888 with population 809524.1503796817\n",
      "loop no 24.0 for tract no 888 with population 809524.3156130761\n",
      "loop no 25.0 for tract no 888 with population 809524.4147532242\n",
      "loop no 26.0 for tract no 888 with population 809524.474237335\n",
      "loop no 27.0 for tract no 888 with population 809524.5099278267\n",
      "loop no 28.0 for tract no 888 with population 809524.5313421313\n",
      "loop no 29.0 for tract no 888 with population 809524.5441907136\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 3.83913 0.2433 219554.3854 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.32383 0.2433 196650.8009 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.23928 0.2433 196659.6774 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.20041 0.2433 196659.6805 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 888 with population 809524.5518998883\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 4.28245 0.2433 219554.3854 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.32383 0.2433 196650.8009 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.23928 0.2433 196659.6819 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.20041 0.2433 196659.6837 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 888, giving up w pop 809524.5518998883\n",
      "loop no 9.0 for tract no 890 with population 783640.0381804942\n",
      "tract,wide, thin angles are  891 136.7905 43.2095\n",
      "loop no 9.0 for tract no 892 with population 784711.4358917015\n",
      "loop no 9.0 for tract no 893 with population 752647.6253356437\n",
      "loop no 10.0 for tract no 893 with population 784534.4844200993\n",
      "I am working on tract number 900 of 3168 tracts\n",
      "loop no 9.0 for tract no 916 with population 763087.4157298116\n",
      "loop no 10.0 for tract no 916 with population 781145.9178127071\n",
      "tract,wide, thin angles are  918 142.2192 37.7808\n",
      "loop no 9.0 for tract no 918 with population 784527.7247583474\n",
      "loop no 9.0 for tract no 919 with population 782545.0832331369\n",
      "I am working on tract number 920 of 3168 tracts\n",
      "loop no 9.0 for tract no 921 with population 783130.0603333842\n",
      "we have 2 non-opposing shorted wedges for tract no 923\n",
      "we have 2 non-opposing shorted wedges for tract no 924\n",
      "loop no 9.0 for tract no 925 with population 684363.2717727395\n",
      "loop no 10.0 for tract no 925 with population 793980.623165054\n",
      "tract,wide, thin angles are  927 108.2149 71.7851\n",
      "loop no 9.0 for tract no 927 with population 782529.6738721861\n",
      "loop no 9.0 for tract no 930 with population 697930.2492449898\n",
      "loop no 10.0 for tract no 930 with population 773597.0252097894\n",
      "loop no 11.0 for tract no 930 with population 783326.3886943595\n",
      "loop no 9.0 for tract no 935 with population 783697.1792151092\n",
      "tract,wide, thin angles are  936 132.388 47.612\n",
      "loop no 9.0 for tract no 937 with population 785160.1596991839\n",
      "we have 2 non-opposing shorted wedges for tract no 938\n",
      "I am working on tract number 940 of 3168 tracts\n",
      "loop no 9.0 for tract no 943 with population 787547.2283680609\n",
      "loop no 9.0 for tract no 949 with population 734399.4718373423\n",
      "loop no 10.0 for tract no 949 with population 744674.424620494\n",
      "loop no 11.0 for tract no 949 with population 746482.6650918218\n",
      "loop no 12.0 for tract no 949 with population 769699.6216361885\n",
      "loop no 13.0 for tract no 949 with population 778284.4859209058\n",
      "loop no 14.0 for tract no 949 with population 782129.6197168543\n",
      "loop no 9.0 for tract no 951 with population 679462.0708779721\n",
      "loop no 10.0 for tract no 951 with population 734526.7958672177\n",
      "loop no 11.0 for tract no 951 with population 768267.3740873637\n",
      "loop no 12.0 for tract no 951 with population 779739.3647682986\n",
      "I am working on tract number 960 of 3168 tracts\n",
      "tract,wide, thin angles are  963 110.2968 69.7032\n",
      "loop no 9.0 for tract no 963 with population 783102.2061002614\n",
      "loop no 9.0 for tract no 965 with population 784179.0598864574\n",
      "loop no 9.0 for tract no 970 with population 784574.358549844\n",
      "loop no 9.0 for tract no 971 with population 788842.3518182428\n",
      "tract,wide, thin angles are  972 102.07 77.93\n",
      "loop no 9.0 for tract no 972 with population 756725.4088500806\n",
      "loop no 10.0 for tract no 972 with population 776586.3753060792\n",
      "loop no 11.0 for tract no 972 with population 782563.1193093238\n",
      "loop no 9.0 for tract no 974 with population 731005.4118878134\n",
      "loop no 10.0 for tract no 974 with population 759235.6034950848\n",
      "loop no 11.0 for tract no 974 with population 762202.286957869\n",
      "loop no 12.0 for tract no 974 with population 777011.846314303\n",
      "loop no 13.0 for tract no 974 with population 782199.8552109969\n",
      "loop no 9.0 for tract no 975 with population 781234.4869741378\n",
      "loop no 9.0 for tract no 977 with population 606223.0069816025\n",
      "loop no 10.0 for tract no 977 with population 686996.1193835689\n",
      "loop no 11.0 for tract no 977 with population 730653.4420282155\n",
      "loop no 12.0 for tract no 977 with population 754682.399879494\n",
      "loop no 13.0 for tract no 977 with population 762333.3987536624\n",
      "loop no 14.0 for tract no 977 with population 786566.3234000672\n",
      "I am working on tract number 980 of 3168 tracts\n",
      "loop no 9.0 for tract no 986 with population 715340.2136956264\n",
      "loop no 10.0 for tract no 986 with population 726283.3236335111\n",
      "loop no 11.0 for tract no 986 with population 727431.3263767599\n",
      "loop no 12.0 for tract no 986 with population 761758.8164852911\n",
      "loop no 13.0 for tract no 986 with population 774196.8777075077\n",
      "loop no 14.0 for tract no 986 with population 779849.0615307661\n",
      "I am working on tract number 1000 of 3168 tracts\n",
      "loop no 9.0 for tract no 1003 with population 803338.8555014185\n",
      "loop no 10.0 for tract no 1003 with population 783795.3326880336\n",
      "loop no 9.0 for tract no 1004 with population 853572.9859718293\n",
      "loop no 10.0 for tract no 1004 with population 854260.2325677061\n",
      "loop no 11.0 for tract no 1004 with population 854294.975048403\n",
      "loop no 12.0 for tract no 1004 with population 854306.8791326032\n",
      "loop no 13.0 for tract no 1004 with population 854311.3972175459\n",
      "loop no 14.0 for tract no 1004 with population 854313.3353627815\n",
      "loop no 15.0 for tract no 1004 with population 854314.2566594792\n",
      "loop no 16.0 for tract no 1004 with population 854314.7551327723\n",
      "loop no 17.0 for tract no 1004 with population 854315.0542709753\n",
      "loop no 18.0 for tract no 1004 with population 854315.2337717519\n",
      "loop no 19.0 for tract no 1004 with population 854315.2022482834\n",
      "loop no 20.0 for tract no 1004 with population 854315.1833340328\n",
      "loop no 21.0 for tract no 1004 with population 854315.1719855212\n",
      "loop no 22.0 for tract no 1004 with population 854315.1651764467\n",
      "loop no 23.0 for tract no 1004 with population 854315.1610909934\n",
      "loop no 24.0 for tract no 1004 with population 854315.1586397213\n",
      "loop no 25.0 for tract no 1004 with population 854315.1571689611\n",
      "loop no 26.0 for tract no 1004 with population 854315.1562865186\n",
      "loop no 27.0 for tract no 1004 with population 854315.1557570443\n",
      "loop no 28.0 for tract no 1004 with population 854315.155439326\n",
      "loop no 29.0 for tract no 1004 with population 854315.1552487595\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 1.39291 0.7028 196657.8161 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 4.22412 0.7028 264343.103 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.91032 0.7028 196657.8137 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.10909 0.7028 196656.4224 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 1004 with population 854315.1560641391\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 1.39291 0.7028 196657.8166 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 4.66745 0.7028 264343.103 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.91032 0.7028 196657.814 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 1.10909 0.7028 196656.4224 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 1004, giving up w pop 854315.1560641391\n",
      "loop no 9.0 for tract no 1006 with population 742311.8979086013\n",
      "loop no 10.0 for tract no 1006 with population 770025.4126829866\n",
      "loop no 11.0 for tract no 1006 with population 778482.565556289\n",
      "loop no 12.0 for tract no 1006 with population 782878.6075550284\n",
      "loop no 9.0 for tract no 1007 with population 814304.6726251632\n",
      "loop no 10.0 for tract no 1007 with population 780587.1786502092\n",
      "I am working on tract number 1020 of 3168 tracts\n",
      "loop no 9.0 for tract no 1024 with population 782431.0495219583\n",
      "loop no 9.0 for tract no 1025 with population 745119.3112760074\n",
      "loop no 10.0 for tract no 1025 with population 765426.9044404511\n",
      "loop no 11.0 for tract no 1025 with population 765509.0410715153\n",
      "loop no 12.0 for tract no 1025 with population 779516.5557558069\n",
      "tract,wide, thin angles are  1026 103.3405 76.6595\n",
      "loop no 9.0 for tract no 1026 with population 779228.5464045643\n",
      "loop no 9.0 for tract no 1027 with population 690191.9280984254\n",
      "loop no 10.0 for tract no 1027 with population 758469.101941014\n",
      "loop no 11.0 for tract no 1027 with population 773168.9307404965\n",
      "loop no 12.0 for tract no 1027 with population 780283.5548902849\n",
      "loop no 9.0 for tract no 1031 with population 783559.527628526\n",
      "loop no 9.0 for tract no 1032 with population 779554.38849296\n",
      "loop no 9.0 for tract no 1034 with population 783839.9316882243\n",
      "tract,wide, thin angles are  1035 133.5199 46.4801\n",
      "I am working on tract number 1040 of 3168 tracts\n",
      "I am working on tract number 1060 of 3168 tracts\n",
      "loop no 9.0 for tract no 1065 with population 787946.0891615634\n",
      "loop no 9.0 for tract no 1074 with population 787454.328642066\n",
      "I am working on tract number 1080 of 3168 tracts\n",
      "tract,wide, thin angles are  1098 108.1574 71.8426\n",
      "I am working on tract number 1100 of 3168 tracts\n",
      "loop no 9.0 for tract no 1102 with population 699827.0769473728\n",
      "loop no 10.0 for tract no 1102 with population 748155.3928622345\n",
      "loop no 11.0 for tract no 1102 with population 772290.762615298\n",
      "loop no 12.0 for tract no 1102 with population 779948.5835459793\n",
      "we have 2 non-opposing shorted wedges for tract no 1103\n",
      "loop no 9.0 for tract no 1114 with population 743696.8448212005\n",
      "loop no 10.0 for tract no 1114 with population 787248.868923239\n",
      "I am working on tract number 1120 of 3168 tracts\n",
      "loop no 9.0 for tract no 1122 with population 782914.2444420503\n",
      "loop no 9.0 for tract no 1123 with population 784204.6378135034\n",
      "tract,wide, thin angles are  1125 144.0 36.0\n",
      "loop no 9.0 for tract no 1125 with population 782015.9891900553\n",
      "loop no 9.0 for tract no 1130 with population 792030.6641559701\n",
      "loop no 9.0 for tract no 1131 with population 755686.5623085978\n",
      "loop no 10.0 for tract no 1131 with population 781639.4816137266\n",
      "loop no 9.0 for tract no 1132 with population 742579.2582919366\n",
      "loop no 10.0 for tract no 1132 with population 775658.4919000079\n",
      "loop no 11.0 for tract no 1132 with population 782667.098348402\n",
      "tract,wide, thin angles are  1134 142.9182 37.0818\n",
      "loop no 9.0 for tract no 1134 with population 782867.4513835823\n",
      "loop no 9.0 for tract no 1139 with population 575266.4644049555\n",
      "loop no 10.0 for tract no 1139 with population 681878.2149496491\n",
      "loop no 11.0 for tract no 1139 with population 723899.2837690362\n",
      "loop no 12.0 for tract no 1139 with population 738752.752788272\n",
      "loop no 13.0 for tract no 1139 with population 762699.3885121965\n",
      "loop no 14.0 for tract no 1139 with population 770017.1900586067\n",
      "loop no 15.0 for tract no 1139 with population 776021.6627982357\n",
      "loop no 16.0 for tract no 1139 with population 781474.0281794586\n",
      "I am working on tract number 1140 of 3168 tracts\n",
      "loop no 9.0 for tract no 1142 with population 786549.9766742818\n",
      "tract,wide, thin angles are  1143 142.5806 37.4194\n",
      "loop no 9.0 for tract no 1143 with population 781158.2775072269\n",
      "loop no 9.0 for tract no 1154 with population 780687.6131194222\n",
      "loop no 9.0 for tract no 1158 with population 778072.5671224972\n",
      "loop no 10.0 for tract no 1158 with population 782814.3009593568\n",
      "loop no 9.0 for tract no 1159 with population 749334.3213775426\n",
      "loop no 10.0 for tract no 1159 with population 775813.7690962746\n",
      "loop no 11.0 for tract no 1159 with population 785000.9454831113\n",
      "I am working on tract number 1160 of 3168 tracts\n",
      "loop no 9.0 for tract no 1160 with population 686447.4622755677\n",
      "loop no 10.0 for tract no 1160 with population 710692.9520807925\n",
      "loop no 11.0 for tract no 1160 with population 721722.9269611875\n",
      "loop no 12.0 for tract no 1160 with population 758780.3722903086\n",
      "loop no 13.0 for tract no 1160 with population 772994.84415906\n",
      "loop no 14.0 for tract no 1160 with population 780645.2408394789\n",
      "tract,wide, thin angles are  1161 136.361 43.639\n",
      "loop no 9.0 for tract no 1161 with population 779330.8754998433\n",
      "loop no 9.0 for tract no 1163 with population 782540.2022506102\n",
      "loop no 9.0 for tract no 1164 with population 780251.933885976\n",
      "loop no 9.0 for tract no 1165 with population 782924.7577757358\n",
      "loop no 9.0 for tract no 1166 with population 790638.1858931305\n",
      "loop no 9.0 for tract no 1169 with population 754683.1261579302\n",
      "loop no 10.0 for tract no 1169 with population 780334.7416099035\n",
      "tract,wide, thin angles are  1170 126.3371 53.6629\n",
      "loop no 9.0 for tract no 1170 with population 732501.1289994468\n",
      "loop no 10.0 for tract no 1170 with population 760635.3162429341\n",
      "loop no 11.0 for tract no 1170 with population 778597.5618742673\n",
      "loop no 12.0 for tract no 1170 with population 783603.8296782598\n",
      "loop no 9.0 for tract no 1171 with population 784900.9628471265\n",
      "loop no 9.0 for tract no 1172 with population 783299.7291176175\n",
      "loop no 9.0 for tract no 1173 with population 720713.7109218459\n",
      "loop no 10.0 for tract no 1173 with population 774386.8473132655\n",
      "loop no 11.0 for tract no 1173 with population 782219.6363764076\n",
      "we have 2 non-opposing shorted wedges for tract no 1174\n",
      "loop no 9.0 for tract no 1175 with population 783914.505002232\n",
      "we have 2 non-opposing shorted wedges for tract no 1178\n",
      "we have 2 non-opposing shorted wedges for tract no 1179\n",
      "I am working on tract number 1180 of 3168 tracts\n",
      "loop no 9.0 for tract no 1180 with population 799045.8895552903\n",
      "loop no 10.0 for tract no 1180 with population 772230.7412630448\n",
      "loop no 11.0 for tract no 1180 with population 780192.7051521479\n",
      "loop no 9.0 for tract no 1181 with population 782203.534642945\n",
      "loop no 9.0 for tract no 1182 with population 782960.3364287717\n",
      "loop no 9.0 for tract no 1192 with population 779201.1960696743\n",
      "loop no 9.0 for tract no 1193 with population 795842.8872153745\n",
      "loop no 10.0 for tract no 1193 with population 788137.2378001036\n",
      "loop no 9.0 for tract no 1195 with population 783096.269373985\n",
      "loop no 9.0 for tract no 1196 with population 783850.4358387913\n",
      "tract,wide, thin angles are  1197 94.7836 85.2164\n",
      "loop no 9.0 for tract no 1197 with population 696918.8282624809\n",
      "loop no 10.0 for tract no 1197 with population 782015.4810570184\n",
      "loop no 9.0 for tract no 1198 with population 784308.4188140368\n",
      "I am working on tract number 1200 of 3168 tracts\n",
      "loop no 9.0 for tract no 1200 with population 901153.6426733633\n",
      "loop no 10.0 for tract no 1200 with population 779913.4005696645\n",
      "loop no 9.0 for tract no 1202 with population 774059.3346811387\n",
      "loop no 10.0 for tract no 1202 with population 783775.2491210705\n",
      "loop no 9.0 for tract no 1204 with population 790095.2105602194\n",
      "we have 2 non-opposing shorted wedges for tract no 1205\n",
      "tract,wide, thin angles are  1206 143.9559 36.0441\n",
      "loop no 9.0 for tract no 1206 with population 781505.6416542656\n",
      "we have 2 non-opposing shorted wedges for tract no 1208\n",
      "we have 2 non-opposing shorted wedges for tract no 1209\n",
      "loop no 9.0 for tract no 1210 with population 798765.2617688371\n",
      "loop no 10.0 for tract no 1210 with population 789923.333834372\n",
      "loop no 9.0 for tract no 1214 with population 780045.0609212513\n",
      "loop no 9.0 for tract no 1219 with population 785702.9950102377\n",
      "I am working on tract number 1220 of 3168 tracts\n",
      "I am working on tract number 1240 of 3168 tracts\n",
      "loop no 9.0 for tract no 1241 with population 790578.4803664241\n",
      "tract,wide, thin angles are  1242 118.7258 61.2742\n",
      "loop no 9.0 for tract no 1242 with population 784243.7395463225\n",
      "loop no 9.0 for tract no 1245 with population 803318.107438461\n",
      "loop no 10.0 for tract no 1245 with population 791229.9290840735\n",
      "I am working on tract number 1260 of 3168 tracts\n",
      "I am working on tract number 1280 of 3168 tracts\n",
      "loop no 9.0 for tract no 1293 with population 759883.1440104624\n",
      "loop no 10.0 for tract no 1293 with population 777746.7348252955\n",
      "loop no 11.0 for tract no 1293 with population 783512.2007339775\n",
      "loop no 9.0 for tract no 1294 with population 778915.9504474441\n",
      "I am working on tract number 1300 of 3168 tracts\n",
      "loop no 9.0 for tract no 1311 with population 798360.9905857106\n",
      "loop no 10.0 for tract no 1311 with population 781233.8270038348\n",
      "loop no 9.0 for tract no 1312 with population 748212.9306545171\n",
      "loop no 10.0 for tract no 1312 with population 776413.8150736993\n",
      "loop no 11.0 for tract no 1312 with population 782880.7952285534\n",
      "I am working on tract number 1320 of 3168 tracts\n",
      "tract,wide, thin angles are  1332 142.2506 37.7494\n",
      "loop no 9.0 for tract no 1335 with population 788510.1860590255\n",
      "loop no 9.0 for tract no 1336 with population 843882.0075924657\n",
      "loop no 10.0 for tract no 1336 with population 843928.6969950236\n",
      "loop no 11.0 for tract no 1336 with population 843899.1120875974\n",
      "loop no 12.0 for tract no 1336 with population 843887.2799605435\n",
      "loop no 13.0 for tract no 1336 with population 843887.3985026113\n",
      "loop no 14.0 for tract no 1336 with population 843940.826831744\n",
      "loop no 15.0 for tract no 1336 with population 843909.5710620886\n",
      "loop no 16.0 for tract no 1336 with population 843887.4861589036\n",
      "loop no 17.0 for tract no 1336 with population 843887.4938200943\n",
      "loop no 18.0 for tract no 1336 with population 843951.7585672595\n",
      "loop no 19.0 for tract no 1336 with population 8.758846736106806\n",
      "loop no 20.0 for tract no 1336 with population 799246.4192131836\n",
      "loop no 21.0 for tract no 1336 with population 828771.4096618766\n",
      "loop no 22.0 for tract no 1336 with population 760123.3132613695\n",
      "loop no 23.0 for tract no 1336 with population 609085.7324792581\n",
      "loop no 24.0 for tract no 1336 with population 680585.2992806819\n",
      "loop no 25.0 for tract no 1336 with population 729934.4280097606\n",
      "loop no 26.0 for tract no 1336 with population 753490.7987199577\n",
      "loop no 27.0 for tract no 1336 with population 766658.5184579974\n",
      "loop no 28.0 for tract no 1336 with population 774915.3360017083\n",
      "loop no 29.0 for tract no 1336 with population 779818.9871441285\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 0.22883 0.25943 192140.7391 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 0.38293 0.25943 195567.7174 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.69427 0.25943 195127.8549 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.25242 0.25943 196982.6757 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 9.0 for tract no 1339 with population 717780.0267896231\n",
      "loop no 10.0 for tract no 1339 with population 759414.7687040704\n",
      "loop no 11.0 for tract no 1339 with population 770949.8951845962\n",
      "loop no 12.0 for tract no 1339 with population 779558.3248949717\n",
      "I am working on tract number 1340 of 3168 tracts\n",
      "tract,wide, thin angles are  1341 115.7679 64.2321\n",
      "loop no 9.0 for tract no 1341 with population 771320.157553114\n",
      "loop no 10.0 for tract no 1341 with population 781862.3960125719\n",
      "loop no 9.0 for tract no 1343 with population 780184.5273478647\n",
      "loop no 9.0 for tract no 1344 with population 801667.1044746729\n",
      "loop no 10.0 for tract no 1344 with population 792503.0387357745\n",
      "loop no 9.0 for tract no 1345 with population 789866.7201255069\n",
      "I am working on tract number 1360 of 3168 tracts\n",
      "loop no 9.0 for tract no 1361 with population 773096.3456493581\n",
      "loop no 10.0 for tract no 1361 with population 777163.1894159757\n",
      "loop no 11.0 for tract no 1361 with population 797015.93149745\n",
      "loop no 12.0 for tract no 1361 with population 789642.3116917547\n",
      "loop no 9.0 for tract no 1363 with population 779400.1394152942\n",
      "loop no 9.0 for tract no 1366 with population 750980.3406741846\n",
      "loop no 10.0 for tract no 1366 with population 760902.5236632271\n",
      "loop no 11.0 for tract no 1366 with population 789716.3555871563\n",
      "loop no 9.0 for tract no 1369 with population 795263.9112455178\n",
      "loop no 10.0 for tract no 1369 with population 797672.6971009915\n",
      "loop no 11.0 for tract no 1369 with population 781306.6302835597\n",
      "loop no 9.0 for tract no 1370 with population 734357.9109271308\n",
      "loop no 10.0 for tract no 1370 with population 776661.6271206184\n",
      "loop no 11.0 for tract no 1370 with population 784081.0839167798\n",
      "loop no 9.0 for tract no 1371 with population 785008.7296209526\n",
      "loop no 9.0 for tract no 1372 with population 785488.722272011\n",
      "loop no 9.0 for tract no 1373 with population 784321.3103876823\n",
      "loop no 9.0 for tract no 1374 with population 785704.5509826145\n",
      "loop no 9.0 for tract no 1375 with population 774734.6709650406\n",
      "loop no 10.0 for tract no 1375 with population 785285.135260035\n",
      "tract,wide, thin angles are  1377 90.4794 89.5206\n",
      "loop no 9.0 for tract no 1377 with population 823409.4072250498\n",
      "loop no 10.0 for tract no 1377 with population 803803.3287452953\n",
      "loop no 11.0 for tract no 1377 with population 793480.3604415066\n",
      "loop no 9.0 for tract no 1378 with population 767621.0919934826\n",
      "loop no 10.0 for tract no 1378 with population 775957.7095815772\n",
      "loop no 11.0 for tract no 1378 with population 785742.7606473744\n",
      "I am working on tract number 1380 of 3168 tracts\n",
      "tract,wide, thin angles are  1386 143.184 36.816\n",
      "loop no 9.0 for tract no 1386 with population 729565.440760659\n",
      "loop no 10.0 for tract no 1386 with population 770555.6471167585\n",
      "loop no 11.0 for tract no 1386 with population 780994.1070859327\n",
      "loop no 9.0 for tract no 1388 with population 785999.6432452581\n",
      "loop no 9.0 for tract no 1389 with population 734685.7134962482\n",
      "loop no 10.0 for tract no 1389 with population 775192.3517964658\n",
      "loop no 11.0 for tract no 1389 with population 784555.929333738\n",
      "loop no 9.0 for tract no 1390 with population 107582.91233639378\n",
      "loop no 10.0 for tract no 1390 with population 235082.11395706367\n",
      "loop no 11.0 for tract no 1390 with population 638355.6786542656\n",
      "loop no 12.0 for tract no 1390 with population 709526.9472606664\n",
      "loop no 13.0 for tract no 1390 with population 758164.9667956228\n",
      "loop no 14.0 for tract no 1390 with population 761929.5448443542\n",
      "loop no 15.0 for tract no 1390 with population 772465.7502844519\n",
      "loop no 16.0 for tract no 1390 with population 777961.5380546022\n",
      "loop no 17.0 for tract no 1390 with population 781464.635135856\n",
      "loop no 9.0 for tract no 1391 with population 748354.0135626728\n",
      "loop no 10.0 for tract no 1391 with population 767854.78312257\n",
      "loop no 11.0 for tract no 1391 with population 780551.0776638017\n",
      "loop no 9.0 for tract no 1392 with population 791732.0381916205\n",
      "loop no 9.0 for tract no 1393 with population 767072.7740909732\n",
      "loop no 10.0 for tract no 1393 with population 779920.5253180095\n",
      "loop no 9.0 for tract no 1397 with population 789017.6629002909\n",
      "loop no 9.0 for tract no 1398 with population 770080.9747736248\n",
      "loop no 10.0 for tract no 1398 with population 783816.8674650132\n",
      "I am working on tract number 1400 of 3168 tracts\n",
      "loop no 9.0 for tract no 1400 with population 768346.2157591805\n",
      "loop no 10.0 for tract no 1400 with population 782213.7164673905\n",
      "we have 2 non-opposing shorted wedges for tract no 1404\n",
      "loop no 9.0 for tract no 1405 with population 784600.8389422356\n",
      "we have 2 non-opposing shorted wedges for tract no 1406\n",
      "we have 2 non-opposing shorted wedges for tract no 1407\n",
      "tract,wide, thin angles are  1413 142.4428 37.5572\n",
      "loop no 9.0 for tract no 1416 with population 770530.001674862\n",
      "loop no 10.0 for tract no 1416 with population 774418.9379632662\n",
      "loop no 11.0 for tract no 1416 with population 782506.279094409\n",
      "loop no 9.0 for tract no 1417 with population 790930.13718228\n",
      "I am working on tract number 1420 of 3168 tracts\n",
      "loop no 9.0 for tract no 1420 with population 682565.258854121\n",
      "loop no 10.0 for tract no 1420 with population 787829.2194560478\n",
      "loop no 9.0 for tract no 1423 with population 784148.3361084645\n",
      "loop no 9.0 for tract no 1425 with population 743411.2228631037\n",
      "loop no 10.0 for tract no 1425 with population 765494.8840976305\n",
      "loop no 11.0 for tract no 1425 with population 776743.0019693756\n",
      "loop no 12.0 for tract no 1425 with population 781506.0891449302\n",
      "loop no 9.0 for tract no 1426 with population 767644.8711451223\n",
      "loop no 10.0 for tract no 1426 with population 809036.0932117202\n",
      "loop no 11.0 for tract no 1426 with population 818223.2732947064\n",
      "loop no 12.0 for tract no 1426 with population 823602.9259002415\n",
      "loop no 13.0 for tract no 1426 with population 826300.8645054746\n",
      "loop no 14.0 for tract no 1426 with population 827732.0420654932\n",
      "loop no 15.0 for tract no 1426 with population 828473.4955410073\n",
      "loop no 16.0 for tract no 1426 with population 828876.1232403901\n",
      "loop no 17.0 for tract no 1426 with population 829117.6334050677\n",
      "loop no 18.0 for tract no 1426 with population 829262.5952467674\n",
      "loop no 19.0 for tract no 1426 with population 829308.6128793493\n",
      "loop no 20.0 for tract no 1426 with population 829336.2199877888\n",
      "loop no 21.0 for tract no 1426 with population 829352.776367139\n",
      "loop no 22.0 for tract no 1426 with population 829362.7077294703\n",
      "loop no 23.0 for tract no 1426 with population 829368.6656595577\n",
      "loop no 24.0 for tract no 1426 with population 829372.2400978549\n",
      "loop no 25.0 for tract no 1426 with population 829374.384646586\n",
      "loop no 26.0 for tract no 1426 with population 829375.671334769\n",
      "loop no 27.0 for tract no 1426 with population 829376.4433328996\n",
      "loop no 28.0 for tract no 1426 with population 829376.9065264784\n",
      "loop no 29.0 for tract no 1426 with population 829377.1844407021\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 1.0979 0.40498 116160.0734 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 4.11749 0.40498 266340.1781 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.82663 0.40498 223592.0266 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.38027 0.40498 223284.9064 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 1426 with population 829377.351188541\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 1.0979 0.40498 116160.0734 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 3.9845 0.40498 266340.1781 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.82663 0.40498 223592.1933 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.38027 0.40498 223284.9064 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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3eDT+5D/2e7x0JREvLy9aNW3I44MewGI20bp5Y8OvVCiEkclPXy5y6qIIrFnd1kURbmpp+P0eL19JtO73GGU9zsPHTgBQo2plunWKICLMRLs2QdxUztgrFQrhSewOcKWUNxANxGutu173/DhgDlBJa3224Et0reu7KNZsjGHr7uu6KNq05vFBD3jMfo/bdu9jdaR1PfCY7btIT8+gZAk/2oa0YnDve4kwm6hXO8DQt+QL4ckcOQMfDewGbKdgSqmaQCfgaAHX5TL/7qJYHx1HYlIyXl5eBDe3dlFEmE0ENW1k+C6K+FMJtmmRPzZt4cKlyyilaNG4Po8O6I3FbN3vsZivr7tLFULYwa4AV0oFAF2A6cCY616aB0wAvi740pwnq4vCulBSjK2Lomb1KpldFCaP2O8xMSmZDTFbbf84HThyHICqlStyh6UtFnMw7UONv1KhEEWVvWfg87EGdZmsJ5RSd2OdTtl6o1+xlVLDgGEAtWrVynOh+WFPF4XFbCIwwPj7Pe74c7/tIuvfKxUWp62pJf27d8ViNtGgTi1DH6cQwirXAFdKdQUStNYxSqmOmc+VBCYDt+c2Xmu9CFgEEBISovNTrCOy66JQShHUtCGPDexDx/AQj+iiOHH6DOtst+Rvsa1U2KxhPYY+eD8RZpPs9yiEh7InvdoBdyulOgN+WOfAPwTqAFln3wFArFIqVGt9ylnF3khOXRTVq1Si620Rmfs9Gr+LIjklhcjY7bZb1bNWKqxcoTy3tGuDJXNjg0oVbnJzpUIIZ8s1wLXWk4BJAJln4OO01t2vf49S6jAQ4soulPT0dLbu3psZZH93UWTt9+gpXRQZGRns2nfItobKpi07uHrNulJhWOsW9Op2O5bwEBobfAMHIYTjDDV/kF0XBUDLJg14ZEAvLGHWLgpP2O8xu5UKm9Svw8De92AxBxMa1IISfsXdXKkQwp0cCnCt9WpgdTbPBxZMOdlbt2kLk2e9/ncXRaUK3G4Jp6PZ5BFdFMkpqWyO25E5/RPL7n3WlQor3GRdqdBiDqFDWDBVK1Vwc6VCiMLEEGfgySkpJJw9D1hb4J4eNYhunSyG7Ve+fqXCNZExRG3ZTkrqVYr5+tImqBlPjxqMxWyiacO6hl6pUAjhXEprlzWGEBISoqOjo/M09kpiEp999ytLln3FwaPHqVyhPP27d6Hf/V0MsbXY2fN/sW7TFtvFx9OZ/yA1qFMLi9mExWzCHCz7PQoh/kspFaO1DvnP80YJ8CwZGRmsjYplybKv+G39Jnx9fOjWycLgPvcS1KxRAVWaf6lXr7J5607WRlrnsnf8+fd+jxGhwdZukbBgw69UKIRwPo8J8OsdOHKc91d+w/Jvf+ZKYhKmlk0Z3PseOt/aweX93Vpr9h8+ZpsW2RizleSUVNt+jxazdTPi5o3qGX6lQiGEa3lkgGe5fCWRlat+YemKrzl0NJ6qlSrQv3tX+t3fmYrlndcPff7CJdZtirW1+J08be2irFsrwLpzvDmYtibZ71EIkT8eHeBZMjIy+H1DNEuXf8XqjdEU8/Xlnjs6MrjPvbRo3CDfn3/12rXr9nuMta1UWK5MadqHtraGdlgwNatXLYCjEUIIqyIR4Nfbf/go7y7/hhWrfiYpOYU2rZoxqM+93HVzO7unV7TWHDwa/4/9HhOTkvH29iK4eRPrxcdwE62aNJRpESGE0xS5AM9y6Uoiy7/5iXeXf82R+JNUrVyRh3p0o+99d2XbP37h0mXbfo/Xr1QYGFCdCLP14mPbkCDKGnwDByGEcRTZAM+Snp7Obxs2s3TZV6yNiqV4MV/uveMWHu7VjdTUa7Y1VOJ2/klGRgZlSpWkXZsgIjJb/GS/RyGEuxT5AL/e3oNHeHfF16xc9QvJKakAeHl5EdSskXVaJMxEUPNGhl+pUAjhGSTAs3Hh0mVW/brWtou8f9kyuQ8SQggXyynAi/Qppn/ZMvS7v4u7yxBCiDyRhTaEEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKgJMCFEMKg7A5wpZS3UmqLUmpV5p9fUEptU0rFKaV+VkrJppFCCOFCjpyBjwZ2X/fnOVrrllrrIGAV8GxBFiaEEOLG7ApwpVQA0AVYnPWc1vrSdW8pBbhuc00hhBB274k5H5gA/GPXX6XUdGAAcBG4ObuBSqlhwDCAWrVq5bVOIYQQ/5LrGbhSqiuQoLWO+fdrWuvJWuuawMfAqOzGa60Xaa1DtNYhlSpVynfBQgghrOyZQmkH3K2UOgwsA25RSn30r/d8AnQv4NqEEELcQK4BrrWepLUO0FoHAn2A37TW/ZRSDa57293AHifVKIQQIhv2zoFnZ6ZSqhGQARwBRhRMSUIIIezhUIBrrVcDqzMfy5SJEEK4kdyJKYQQBiUBLoQQBiUBLoTIl7id0cxZOJX09HR3l1Lk5OciphBCsGLVB7y/ciFaayY88ry7yylS5AxcCJEvJ08fB+DVJTP44fev3FtMESNn4EKIfDmZEE+7NjeTlJzI6OcGUT+wEQ3qNHF3WUWCnIELIfLlZEI8gQH1WDRrGSX8SjJobA8uXbno7rKKBAlwIUSepV5N5ez5BKpVqUH1KgG8PfNTjsYfYvSzA8nIyHB3eR5PAlwIkWenEuIBqF45AABzcAemjpnLz2tX8eqSl9xZWpEgAS6EyLOTmQFerXIN23MP93qEnl37M/ftafyy7jt3lVYkSIALIfLsZIK1A6Valb8DXCnFjIkLaNkkmMeeeYgDR/a6qzyPJwEuhMizk6ezzsAD/vF8Cb8SLJ6zAl/fYgwe14MriZfdUZ7HkwAXQuTZyYR4ypYuR+lSZf7zWo2qtVg48xMOHt3Hk1MHo7XsuljQJMCFEHl2MuH4P+a//61dSEeeeXwm3//+FQvem+3CyooGCXAhRJ6dPB1PtSoBN3zP0Acf5747+zDrzWf5fcNPLqqsaJAAF0Lk2YnTNz4DB+tFzTnPLKRJgxaMnNyfw8cPuKg6zycBLoTIk6vXrnLm/Gmq53IGDlDCryRL5qxEeXkxZFxPkpITXVCh55MAF0LkScLZk2it/9OBkpNaNerw1vSP+PPgLsZOGyYXNQuABLgQIk9sLYRVbjyFcr0I821MGvki3/yykrc/mues0ooMCXAhRJ4kpyQBcPiYY3PajwwYS9fbujP99adZG/U/Z5RWZEiACyHypF2bm4kIu43n541ny47Ndo9TSvHKs+/QsE4THnm6L8dOHHZekR5OAlwIkSfe3t68Mf1DqlSqzpAJvThz7rTdY0uVLM3iuSvRGRkMHtfTdjYvHCMBLoTIs/L+FVg8ZwUXLp5n+MQHuJZ2ze6xdWrW5/UX3mfXvm1MmP6IXNTMAwlwIUS+NG8UxNwpC4na8gcvzH/KobG3tr+L8SOm8sUPn7Jk2QInVei5JMCFEPl2350PMPTB0SxZtoCVqz50aOxjA5/irpvvYdr8CWyIXuOkCj2TBLgQokA88/gMwk0WJs4YybbdsXaP8/LyYt5zS6hTsz4jJj1I/KljTqzSs0iACyEKhI+PD2/P/ITy/pUYMr4X5/46Y/fYMqXLsmTuZ6ReTWHohF6kpKY4sVLPIQEuhCgwFW6qxJK5Kzl7/jSPPN2PtLQ0u8fWD2zE69PeY+uuGCbNHCUXNe1gd4ArpbyVUluUUqsy/zxHKbVHKbVNKfWlUsrfaVUKIQyjZZNgZkx6g/Wbf+elBZMdGnu7pRtjhj7Dim8/4P3P3nZShZ7DkTPw0cDu6/78C9Bca90S2AtMKsjChBDG1bvbAAb2epS3P5rHVz8uc2jsk0Of4bYOnXlu7hg2xa13UoWewa4AV0oFAF2AxVnPaa1/1lpn/X4UCdi3oo0Qokh4bswcwlq3Z+wLw9m5d6vd47y8vHht2nvUrBHIsKf62DZOFv9l7xn4fGACkJHD64OAH7J7QSk1TCkVrZSKPnPG/osaQghj8/Xx5e2Zn+JfrjxDxvfir4vn7R5brow/S+d+RlJyIsOe6kPq1VQnVmpcuQa4UqorkKC1jsnh9clAGvBxdq9rrRdprUO01iGVKlXKV7FCCGOpVKEK78xezqmEeEZO7k96errdYxvWbcr8qUuI3R7FlDlPOrFK47LnDLwdcLdS6jCwDLhFKfURgFLqIaAr0FfLJWMhRDaCm4cy/anXWBP5C7PfetahsZ1vuY/HBj7Fx18u5qMvFuc+oIjJNcC11pO01gFa60CgD/Cb1rqfUupO4Cngbq21rEQjhMjRg/cOot/9Q1nw3hy+/fUzh8aOHzGVjuG388zs0cRsj3JShcaUnz7wBUAZ4BelVJxSamEB1SSE8EDTxr2CqaWZMc8PZc/+HXaP8/b2ZsGLH1C9Sk2GTehNwtlTTqzSWJQrZz5CQkJ0dHS0y75PCFG4nDpzgrv6mSlZsjTff7CBcmX87R67a9827h4YQYvGrVn+1k8U8y3mvEILGaVUjNY65N/Py52YQgiXqVqpOotmLeP4icOMeuYhMjJyamz7r6YNWvLys4vYFLee518Z78QqjUMCXAjhUm2C2jJt/Dx+W/8DLy+a5tDYe27vxYj+Y3hv5Vss/+Z9J1VoHBLgQgiXG9B9GH3ufpj5i1/ix9VfOzR20sgXaR96C5NmjmLrrmy7m4sMCXAh3KwoduAqpZj+1GsENQ1h9HOD2H94j91jfXx8eOulj6hUoSpDxvfi7PkEJ1ZauEmAC+FGl6+kEtBmBk06vuzuUlzOr7gfi2Yvp3gxPwaN7cHlK5fsHlvevyJL5qzg/IUzjJj4oENbuXkSCXAh3MjPzweAS1dSWfa1/euFeIoaVWvy9sxPOXz8AKOfG+jQRc3mjVsze/JbbIxdy4uvFs219CTAhXAjXx9vfls+FICxL3zH/sPn3FyR64WbInjuyTn8tOZbXl06w6Gx3Tv3ZfADj7H409f4/PtsV/PwaBLgQrhZo3qVeGniHQBYerxN6lX7N0HwFIN6j6R75768/PY0fln3nUNjp4yeSXhwBBOmP8qOPVucVGHhJAEuRCHwUA8T5uCaANRtO9vN1bieUopZT79Js4ateHzKwxw8us/usb4+viyc+Qk3+Vdg8PhenL9w1omVFi4S4EIUEp8v6m97PGS8Y+uFeIISfiVYMncl3t4+DB7XgyuJl+0eW7F8ZRbPXsGZc6cc3srNyCTAhShE9q0bB8APv+/lix/sXy/EUwRUq81bMz5m/+E/efL5IQ61WAY1C2HGpDf4Y9NvzHzjGSdWWXhIgAtRiJQsUYyfPh4MwGNTvuHw8b/cXJHrdQi9hcmPz+D7377kjffnODS2d7cBPNRzBG99+Apf/7zCSRUWHhLgQhQyzRtVYeqY2wBod+9bXEuzfxMETzG87xPcc3svZr4xhdUbf3Zo7NQxc2nTqi1jpw1j9/7tTqqwcJAAF6IQGvpgKK2aVgMg0DzLzdW4nlKKuVPepnH95oyc3J8jxw/aPbaYbzEWzVpGuTL+DB7b06Gt3IxGAlyIQur7DwbaHo96xrH1QjxByRKlWDJnJQCDx/ckKTnR7rGVK1Zl0ezlnDh9jMemPOTQVm5GIgEuRCG2Z/VYAL78cSerft3t5mpcr3ZAXd6Y/iF79u9g3AvDHbqoaWoRxosTXuX3DT8xZ+FU5xXpRhLgQhRiZUoX57v3HwZg+MQvOX7yonsLcoOO4bczceQLfP3zCt7+eL5DY/vdP4S+9w3m9Xdn8f1vXzqnQDeSABeikAtqVp1JozoCENbtDdLS7F8vxFOMfGg8XW69n+mvTWLdpt8cGvvC+Pm0bh7KE1MHs/fgLidV6B4S4EIYwKiH29KwbkUAaptnurka11NKMe+5xTQIbMwjk/py/OQRu8cWL1acd2Yvp2SJUgwa14NLVzzntxgJcCEMImvRK4BxLzi2XognKFWyNIvnriQ9PY3B43qSnJJs99hqlWuwaNYyjsUf5rEpjm3lVphJgAthEEopdv0+BoBPv97Kz2v2urki16tbqwGvv/g+O/duZcL0Rxy6qBka1I7nx77Mr+u+Z947LzqxSteRABfCQMqV8eOrJQMAGDj2M04m2L9eiKe4rX1nxg5/li9++ISly99waOxDPUfQq9sAXnnnRX5e862TKnQdCXAhDKZNqwDGDG0PQEjn10lP94zpAEeMHjSJOyzdeH7eeDbGrLV7nFKKGRMX0KqpicefHcj+w386sUrnkwAXwoDGDo+gZvVyADSMmOvmalzPy8uLV59/l8CAegyf+ADxp47ZPdavuB/vzF5BsWLFGTzOsa3cChsJcCEMauPXjwKQkprG5Fk/ubka1ytTuixLX/6M1KspDJvQm5TUFLvH1qhak4UzPuHQsf08MXWQYS9qSoALYVBKKbb/+gQA762M4fcNB9xbkBvUD2zMq88vJW5XNE/PfMyhi5ptQyxMGT2LH1d/w+vvGnO9GQlwIQysvH9JVi7sC0C/x5dz5twVN1fkend2vIcnhjzN8m/f54PPFzk0dsgDj3H/XQ8wZ+FUflv/o5MqdB4JcCEMrm1IbUY+FA5A0B2vkZFh/1mopxg77FluaXcXz80dw+a4DXaPU0oxe/JbNG3QklHPDODQsf1OrLLg2R3gSilvpdQWpdSqzD/3VErtVEplKKVCnFeiECI3Tz92MxVuKglAy07z3VuMG3h5ebHgxfepUa02w57qw6kzJ+weW8KvJEvmrkR5eTFkXE8Sk4zzW4wjZ+CjgeuXQ9sB3A/Y38MjhHCauJ9GA/DXxWReePV/bq7G9cqV8WfJnBVcSbrMsKf6kHo11e6xNasH8tZLH7P30G7GTBvq0Fy6O9kV4EqpAKALsDjrOa31bq21sZsohfAgXl6KrT9bQ3zhh1H8sfmwewu6gV37EoneVvDte43rN2fec4uJ2RbJs3PHODQ2IuxWnh41nVW/fs5bH7xc4LU5g71n4POBCYAxe22EKCIqli/FJwv6AND7kU84fyHJzRVZXbqSxowFh6kRsp4aIevp9EAc9wzaTnJKwW+00PW27ox6eDwfffEOn3y11KGxI/qPoVunHsx44xnWRv5a4LUVtFwDXCnVFUjQWsfk5QuUUsOUUtFKqegzZ87k5SOEEA6wmOsy+IE2ALS4bb5bpgO01nz761lCu0ZTI2Q9TTpGseC9eNvrpUp68eXiFpTw83bK9094ZBoWcycmz3qc2B2b7B6nlOKVZ9+hUd2mPDK5H0fjDzmlvoKicvs/Vyk1A+gPpAF+QFngC611v8zXVwPjtNbRuX1ZSEiIjo7O9W1CiALQoMMckpKvUb1KWTZ/N8rp37f/cBLTXzvCz2uz34Oyf/eqjBlak8oVizm9FoC/Lp6n84Bwrl67yg8fbKRyxap2jz18/ACd+4dTo1otvlm6lhJ+JZ1Yae6UUjFa6/80i+R6Bq61nqS1DtBaBwJ9gN+ywlsIUXj9uWYcACdOX2L2W2sK/PMTk9J5ZdFR27SIpceWf4R36+al+eKd5sRHtyM+uh0zJ9VzWXgD3FSuPIvnrODCxfMMn/gAV69dtXtsYEA93pj+Ibv3bWf8iyMK7UXNPPeBK6XuU0odB8KB75RSRe9eXiEKMS8vRcwPjwHw6pL1bIqzf72Q7Git+XnNOdrdF0ONkPU0jIjk5UV/f2YxX8UL4+pwaGM48dHtWPVeK8Jal8vXd+ZXs4ateHnK22yKW8+0eRMcGntz2zuY8MjzfPnjMhZ/+rqTKsyfXKdQCpJMoQjher+s28fDT1p3d9/525P4ly3h0PhPvjrF+Bezv02/d7fKjBtRi+pViue7TmeaNv8p3v5oHvOmLqZX1wF2j9NaM2xCb35a+y2fvvED7UI6Oq/IG8jzFIoQwtg6dWhAv/tbA9DslnkOTwecSvh76qFZw1Ise7OZbVrklecaFPrwBnh61HTatbmZiS+NZNvuWLvHKaWYN3UJdWs1YMTEB4k/ddSJVTpOzsCFKCIC2ryE1lCvdnnWfj7C7nFpaRqlwNtbObE65zt/4Sx39jOjtebHjyKpcFMlu8ceOLKXLgPaUqdWfb5453dK+Dn2W0x+yRm4EEXc4Y0TAThw5DyvLV1v9zgfH2X48AYo71+RJXNXcv7CGYZPfJC0tDS7x9ar3ZDXXniPbbtjmTRzVKG5qCkBLkQR4ePjxaZV1nbCWW+uIXZHfC4jPE+Lxq2Z9fSbbIxZw4uvTXJo7O0RXRk7bAorV33I+ysXOqlCx0iAC1GE1Khalndm3w9At4ff5/IV+9cL8RQ9uvRjcJ9RvPPJq3z546cOjX1iyGQ6dejCcy+PJWrLH06q0H4S4EIUMZ1vaUz3zs0BaNzx5UIzHeBKU56YhTm4A+NeGMGOP+PsHufl5cVrL7xHrRp1GPZUH06cPu68Iu2px63fLoRwi9em3W17fGc/x9YL8QS+Pr4snPEJ/uXKM2R8L85fOGf32LKly7H05c9ITkli2ITeDq16WNAkwIUoog5HPgXAjj9P8/ZHUW6uxvUqVajC4tkrOH3mBCMn9yc93f6FtRrUacL8qUvYsnMzz8we7cQqb0wCXIgiytfH27Yx8rT5/2P7nlNursj1Wjdvw4yJC1gb9Suz3pzi0NjOt9zH44Mm8slXS/noi8W5D3ACCXAhirBaNfx5c/o9gHUqJTHJ/vVCPEWfex5mQI/hvPH+XPo/frdDu9uPG/4ct7S9k2dmjyZ6W6QTq8ye3MgjhGDYU1/w3f/2ABAf/bSbq3G91Kup1G1bBrD2i/e7fwgDegynWuUauY69cOkvugxoS3JKEj98FEmVitUKvD65kUcIkaNFs+63Pb538AdurMQ9ihcrzq7fE/j0jR9o0yqc19+dhblbAx6Z1JfNWzfesFPHv+xNLJ67gktXLjJsQh+HVj3MLwlwIQQAhzZaL2pu3nqc91YUvd+Uy5XxJyLsVpa+/Dnrv9zNoD6jWL3xZ+4dbKHzgHBWrvowx46TJvVb8Mpz7xC9bSNTXxnnspolwIUQABTz9Wbt58MBmDz7Z3bvT3BzRe5TO6Auzz05m+jvD/HSxNdJSk7kiamDCe1ajzkLp2a76/3dnXry6ICxvL9yIcu+fs8ldcocuBDiH1as2saTU1cBsP+P8ZTw83VzRe6ntWZd1P9YsnwB//vjB7y9vOnWqQeD+owiuHmo7X3p6en0e7wbkbFr+eKd32ndvE2BfH9Oc+AS4EKI/xjwxAr+98d+oGhe1LyRQ8f2896KhSz/5j0uJ16idbM2DOozkq639aCYbzHOXzhH5wHhXEu7xo8fRlKpQpV8f6dcxBRC2O2D+b1sjzdvde/t4oVNnZr1eX7sXKK/P8T0Ca9y8fIFHpvyMGFd6/PKohdIS7tm28ptxKQHuZZ2zWm1yBm4ECJbqVfTWPXrHjrf0kimUW4gIyODtVG/suTTBfy24Ud8fXzp1qkn5f0rsvjT1xj8wGNMG/tyvr5DplCEEMLJDhzZy3sr3mL5t++TmHTF9vyrzy+lR5e87wUvAS6EEC5y+colVqz6gKXL3+Twsf34Ffdj3Re7qF4lIE+fl1OA++S7UiGEEP9QpnRZBvcZxcBej/L7hp/4fcNPlCxRqsC/RwJcCCGcxMvLi1vb38Wt7e9yzuc75VOFEEI4nQS4EEIYlAS4EEIYlAS4EEIYlAS4EEIYlAS4EEIYlAS4EEIYlAS4EEIYlEtvpVdKnQGOuOwL7VMROOvuItxEjr1oKsrHDsY8/tpa60r/ftKlAV4YKaWis1tjoCiQY5djL4o86fhlCkUIIQxKAlwIIQxKAhwWubsAN5JjL5qK8rGDBx1/kZ8DF0IIo5IzcCGEMCgJcCGEMKgiGeBKqSClVKRSKk4pFa2UCv3X67WUUleUUuPcVaMz5XT8SqnQzOfilFJblVL3ubvWgnaDY++klIpRSm3P/N9b3F1rQbvBsVdQSv2e+Xd+gbvrdIYb/cwrpSYppfYrpf5USt3hzjodprUucv8BPwN3ZT7uDKz+1+ufAyuBce6u1ZXHD5QEfDIfVwMSsv7sKf/d4NhbA9UzHzcH4t1dqwuPvRTQHhgBLHB3nS4+9qbAVqA4UAc4AHi7u157/yuSZ+CABspmPi4HnMh6QSl1L3AQ2On6slwm2+PXWidprdMyn/fLfJ+nyenYt2its/4e7AT8lFLF3VCfM+V07Ila6z+AFHcV5gI5/czfAyzTWqdqrQ8B+4HQbMYXSkV1T8wngJ+UUnOxTiO1BVBKlQKeAjoBHjl9kukJsjl+AKVUGLAUqA30vy7QPcUT5HDs1+kObNFap7qyMBd4gtyP3VM9QfbHXgOIvO59xzOfMwSPDXCl1K9A1WxemgzcCjyptf5cKdULWALcBjwPzNNaX1FKua5YJ8jj8aO1jgKaKaWaAO8rpX7QWhvqzCyvx545thkwC7jdFbUWtPwcu9Hl8diz+0E3zG+eRbIPXCl1EfDXWmtlTeqLWuuySql1QM3Mt/kDGcCzWmuPurCT0/Fn877fgfFa62iXF+kkNzp2pVQA8BswUGu93p11OkNu/78rpR4GQrTWo9xVo7Pc4Gd+EoDWekbm+34CpmqtN7qxXLsV1TnwE4Al8/EtwD4ArXUHrXWg1joQmA+85GnhnSnb41dK1VFK+WQ+rg00Ag67o0AnyunY/YHvgEmeGN6Zsj32IiKnY/8G6KOUKq6UqgM0ADa5ob488dgplFwMBV7NDKsUYJib63G1nI6/PTBRKXUN628fj2qtjbbsZm5yOvZRQH1gilJqSuZzt2utE9xQo7Pk+PdeKXUY60W+YpkX8m/XWu9yR5FOku2xa613KqVWALuANGCk1jrdfWU6pkhOoQghhCcoqlMoQghheBLgQghhUBLgQghhUBLgQghhUBLgQghhUBLgQghhUBLgQghhUP8H1aChevCDWqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 1426, giving up w pop 829377.351188541\n",
      "loop no 9.0 for tract no 1427 with population 779770.8192855142\n",
      "we have 2 non-opposing shorted wedges for tract no 1428\n",
      "loop no 9.0 for tract no 1429 with population 781297.2534562288\n",
      "loop no 9.0 for tract no 1430 with population 779248.0569479046\n",
      "loop no 9.0 for tract no 1432 with population 885598.882061725\n",
      "loop no 10.0 for tract no 1432 with population 750748.81208613\n",
      "loop no 11.0 for tract no 1432 with population 771838.533518435\n",
      "loop no 12.0 for tract no 1432 with population 779809.6918539333\n",
      "we have 2 non-opposing shorted wedges for tract no 1433\n",
      "loop no 9.0 for tract no 1433 with population 784131.3196504401\n",
      "loop no 9.0 for tract no 1434 with population 564244.8109483605\n",
      "loop no 10.0 for tract no 1434 with population 688811.6650628401\n",
      "loop no 11.0 for tract no 1434 with population 794061.7787543143\n",
      "loop no 9.0 for tract no 1437 with population 783839.4719247157\n",
      "loop no 9.0 for tract no 1438 with population 784292.0584362694\n",
      "we have 2 non-opposing shorted wedges for tract no 1439\n",
      "I am working on tract number 1440 of 3168 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1440\n",
      "loop no 9.0 for tract no 1441 with population 784548.8094201516\n",
      "we have 2 non-opposing shorted wedges for tract no 1442\n",
      "loop no 9.0 for tract no 1443 with population 785910.8925687799\n",
      "loop no 9.0 for tract no 1444 with population 740804.9639012809\n",
      "loop no 10.0 for tract no 1444 with population 790940.3781896016\n",
      "we have 2 non-opposing shorted wedges for tract no 1448\n",
      "tract,wide, thin angles are  1449 120.6222 59.3778\n",
      "loop no 9.0 for tract no 1451 with population 782251.243860773\n",
      "loop no 9.0 for tract no 1452 with population 813652.8921819925\n",
      "loop no 10.0 for tract no 1452 with population 797677.0163433225\n",
      "loop no 11.0 for tract no 1452 with population 789409.7148036768\n",
      "loop no 9.0 for tract no 1455 with population 786474.4126937841\n",
      "tract,wide, thin angles are  1458 114.3463 65.6537\n",
      "loop no 9.0 for tract no 1458 with population 774607.2832672913\n",
      "loop no 10.0 for tract no 1458 with population 782737.9642978883\n",
      "loop no 9.0 for tract no 1459 with population 782400.8997908963\n",
      "I am working on tract number 1460 of 3168 tracts\n",
      "loop no 9.0 for tract no 1460 with population 684341.413936752\n",
      "loop no 10.0 for tract no 1460 with population 744608.1497602015\n",
      "loop no 11.0 for tract no 1460 with population 792465.124674437\n",
      "loop no 9.0 for tract no 1462 with population 766555.9853813762\n",
      "loop no 10.0 for tract no 1462 with population 778860.0224514054\n",
      "loop no 9.0 for tract no 1463 with population 788871.4669842818\n",
      "loop no 9.0 for tract no 1466 with population 777257.6340581098\n",
      "loop no 10.0 for tract no 1466 with population 783465.0728138107\n",
      "tract,wide, thin angles are  1467 94.7529 85.2471\n",
      "loop no 9.0 for tract no 1467 with population 783949.9958789509\n",
      "I am working on tract number 1480 of 3168 tracts\n",
      "loop no 9.0 for tract no 1484 with population 771873.0100672492\n",
      "loop no 10.0 for tract no 1484 with population 780430.3667217728\n",
      "tract,wide, thin angles are  1485 137.8752 42.1248\n",
      "loop no 9.0 for tract no 1485 with population 383965.8836412053\n",
      "loop no 10.0 for tract no 1485 with population 577610.0056225972\n",
      "loop no 11.0 for tract no 1485 with population 667423.2897323819\n",
      "loop no 12.0 for tract no 1485 with population 703553.8163279067\n",
      "loop no 13.0 for tract no 1485 with population 726791.821347053\n",
      "loop no 14.0 for tract no 1485 with population 747471.9227863087\n",
      "loop no 15.0 for tract no 1485 with population 761944.6219263563\n",
      "loop no 16.0 for tract no 1485 with population 770033.5866325444\n",
      "loop no 17.0 for tract no 1485 with population 776568.9621831356\n",
      "loop no 18.0 for tract no 1485 with population 780398.1186486031\n",
      "loop no 9.0 for tract no 1486 with population 788733.376038834\n",
      "loop no 9.0 for tract no 1488 with population 776541.5482107134\n",
      "loop no 10.0 for tract no 1488 with population 783783.9719575538\n",
      "loop no 9.0 for tract no 1489 with population 746378.434253687\n",
      "loop no 10.0 for tract no 1489 with population 776358.9628195406\n",
      "loop no 11.0 for tract no 1489 with population 782169.5632940491\n",
      "loop no 9.0 for tract no 1490 with population 741196.15992269\n",
      "loop no 10.0 for tract no 1490 with population 774008.4988169033\n",
      "loop no 11.0 for tract no 1490 with population 781671.6005190345\n",
      "loop no 9.0 for tract no 1491 with population 757933.0993656142\n",
      "loop no 10.0 for tract no 1491 with population 781728.6342727867\n",
      "I am working on tract number 1500 of 3168 tracts\n",
      "tract,wide, thin angles are  1503 119.0112 60.9888\n",
      "loop no 9.0 for tract no 1503 with population 773553.067075423\n",
      "loop no 10.0 for tract no 1503 with population 779398.5638314586\n",
      "loop no 9.0 for tract no 1506 with population 707317.679306497\n",
      "loop no 10.0 for tract no 1506 with population 746815.2678559766\n",
      "loop no 11.0 for tract no 1506 with population 773948.3503490309\n",
      "loop no 12.0 for tract no 1506 with population 780774.8981376331\n",
      "loop no 9.0 for tract no 1507 with population 718451.0138056364\n",
      "loop no 10.0 for tract no 1507 with population 763828.6995175941\n",
      "loop no 11.0 for tract no 1507 with population 773209.1416078032\n",
      "loop no 12.0 for tract no 1507 with population 777539.1353294349\n",
      "loop no 13.0 for tract no 1507 with population 778251.6757387668\n",
      "loop no 14.0 for tract no 1507 with population 782184.0241545828\n",
      "loop no 9.0 for tract no 1511 with population 753143.0673789978\n",
      "loop no 10.0 for tract no 1511 with population 780927.7959570626\n",
      "tract,wide, thin angles are  1512 115.4561 64.5439\n",
      "loop no 9.0 for tract no 1515 with population 771038.8658922907\n",
      "loop no 10.0 for tract no 1515 with population 779776.8185200053\n",
      "we have 2 non-opposing shorted wedges for tract no 1516\n",
      "loop no 9.0 for tract no 1517 with population 688133.4642676287\n",
      "loop no 10.0 for tract no 1517 with population 755425.7131096306\n",
      "loop no 11.0 for tract no 1517 with population 776707.5404156162\n",
      "loop no 12.0 for tract no 1517 with population 781367.3936039705\n",
      "I am working on tract number 1520 of 3168 tracts\n",
      "loop no 9.0 for tract no 1532 with population 781112.5544622194\n",
      "loop no 9.0 for tract no 1538 with population 758797.6642721061\n",
      "loop no 10.0 for tract no 1538 with population 773236.1397599605\n",
      "loop no 11.0 for tract no 1538 with population 785757.5447087205\n",
      "tract,wide, thin angles are  1539 140.9213 39.0787\n",
      "loop no 9.0 for tract no 1539 with population 782483.6871400794\n",
      "I am working on tract number 1540 of 3168 tracts\n",
      "loop no 9.0 for tract no 1541 with population 763626.6676091828\n",
      "loop no 10.0 for tract no 1541 with population 779762.4447558131\n",
      "loop no 9.0 for tract no 1542 with population 677519.1926553138\n",
      "loop no 10.0 for tract no 1542 with population 765165.2310943706\n",
      "loop no 11.0 for tract no 1542 with population 778990.3033657849\n",
      "loop no 9.0 for tract no 1544 with population 766931.2244184258\n",
      "loop no 10.0 for tract no 1544 with population 779102.5245216781\n",
      "loop no 9.0 for tract no 1545 with population 781270.3874005931\n",
      "loop no 9.0 for tract no 1551 with population 782256.3941016723\n",
      "loop no 9.0 for tract no 1552 with population 741249.6926506644\n",
      "loop no 10.0 for tract no 1552 with population 785189.5836034659\n",
      "loop no 9.0 for tract no 1553 with population 756495.1216753122\n",
      "loop no 10.0 for tract no 1553 with population 790341.5313948431\n",
      "I am working on tract number 1560 of 3168 tracts\n",
      "loop no 9.0 for tract no 1562 with population 787535.2134785295\n",
      "loop no 9.0 for tract no 1568 with population 823978.8414474349\n",
      "loop no 10.0 for tract no 1568 with population 815581.314905931\n",
      "loop no 11.0 for tract no 1568 with population 805012.2676494027\n",
      "loop no 12.0 for tract no 1568 with population 772398.2569514835\n",
      "loop no 13.0 for tract no 1568 with population 779788.5601267691\n",
      "loop no 9.0 for tract no 1569 with population 736589.8917218673\n",
      "loop no 10.0 for tract no 1569 with population 774015.1456214607\n",
      "loop no 11.0 for tract no 1569 with population 782155.9738289766\n",
      "loop no 9.0 for tract no 1571 with population 785631.7861955131\n",
      "loop no 9.0 for tract no 1573 with population 753318.3036580003\n",
      "loop no 10.0 for tract no 1573 with population 769560.3193785573\n",
      "loop no 11.0 for tract no 1573 with population 779361.3015252909\n",
      "loop no 9.0 for tract no 1574 with population 655423.4124223777\n",
      "loop no 10.0 for tract no 1574 with population 699885.3632160351\n",
      "loop no 11.0 for tract no 1574 with population 740365.3752079267\n",
      "loop no 12.0 for tract no 1574 with population 762889.9580558095\n",
      "loop no 13.0 for tract no 1574 with population 774392.5847589144\n",
      "loop no 14.0 for tract no 1574 with population 778891.2727111977\n",
      "tract,wide, thin angles are  1575 135.765 44.235\n",
      "loop no 9.0 for tract no 1575 with population 690000.1655510707\n",
      "loop no 10.0 for tract no 1575 with population 749037.740894841\n",
      "loop no 11.0 for tract no 1575 with population 768816.0818594206\n",
      "loop no 12.0 for tract no 1575 with population 777119.8564775957\n",
      "loop no 13.0 for tract no 1575 with population 781461.7025199594\n",
      "loop no 9.0 for tract no 1576 with population 653071.038308332\n",
      "loop no 10.0 for tract no 1576 with population 682028.1921926785\n",
      "loop no 11.0 for tract no 1576 with population 728861.0378711249\n",
      "loop no 12.0 for tract no 1576 with population 749970.0209914027\n",
      "loop no 13.0 for tract no 1576 with population 765099.3272449485\n",
      "loop no 14.0 for tract no 1576 with population 775270.2909104081\n",
      "loop no 15.0 for tract no 1576 with population 779859.3487721412\n",
      "I am working on tract number 1580 of 3168 tracts\n",
      "loop no 9.0 for tract no 1580 with population 781544.49882125\n",
      "loop no 9.0 for tract no 1582 with population 783320.4703035806\n",
      "loop no 9.0 for tract no 1594 with population 782794.7030193947\n",
      "I am working on tract number 1600 of 3168 tracts\n",
      "loop no 9.0 for tract no 1606 with population 783344.7218110198\n",
      "tract,wide, thin angles are  1611 131.4526 48.5474\n",
      "loop no 9.0 for tract no 1619 with population 781293.3314372608\n",
      "I am working on tract number 1620 of 3168 tracts\n",
      "tract,wide, thin angles are  1620 99.9937 80.0063\n",
      "loop no 9.0 for tract no 1620 with population 719731.4259804168\n",
      "loop no 10.0 for tract no 1620 with population 764024.2195362942\n",
      "loop no 11.0 for tract no 1620 with population 776740.6537060358\n",
      "loop no 12.0 for tract no 1620 with population 782573.1113684243\n",
      "loop no 9.0 for tract no 1626 with population 763029.3062229701\n",
      "loop no 10.0 for tract no 1626 with population 783247.66332969\n",
      "loop no 9.0 for tract no 1634 with population 781869.2172627468\n",
      "loop no 9.0 for tract no 1635 with population 778572.4017862243\n",
      "loop no 10.0 for tract no 1635 with population 788167.6562198354\n",
      "loop no 9.0 for tract no 1636 with population 745139.651941132\n",
      "loop no 10.0 for tract no 1636 with population 779760.8820006985\n",
      "I am working on tract number 1640 of 3168 tracts\n",
      "loop no 9.0 for tract no 1646 with population 765099.6939136301\n",
      "loop no 10.0 for tract no 1646 with population 780299.0537325043\n",
      "tract,wide, thin angles are  1647 142.9442 37.0558\n",
      "loop no 9.0 for tract no 1648 with population 782895.55388909\n",
      "loop no 9.0 for tract no 1653 with population 762616.3700814437\n",
      "loop no 10.0 for tract no 1653 with population 777070.6612556595\n",
      "loop no 11.0 for tract no 1653 with population 782388.2809298635\n",
      "loop no 9.0 for tract no 1655 with population 756511.3932089112\n",
      "loop no 10.0 for tract no 1655 with population 776668.3874156113\n",
      "loop no 11.0 for tract no 1655 with population 783067.3657324769\n",
      "tract,wide, thin angles are  1656 143.58 36.42\n",
      "loop no 9.0 for tract no 1656 with population 779036.2330719696\n",
      "loop no 9.0 for tract no 1657 with population 716152.2276677466\n",
      "loop no 10.0 for tract no 1657 with population 762916.4104501586\n",
      "loop no 11.0 for tract no 1657 with population 776857.3867667939\n",
      "loop no 12.0 for tract no 1657 with population 782186.1792344559\n",
      "loop no 9.0 for tract no 1658 with population 734856.1359298122\n",
      "loop no 10.0 for tract no 1658 with population 761198.9819267553\n",
      "loop no 11.0 for tract no 1658 with population 777373.705699737\n",
      "loop no 12.0 for tract no 1658 with population 783143.0386395669\n",
      "I am working on tract number 1660 of 3168 tracts\n",
      "loop no 9.0 for tract no 1662 with population 736172.181824214\n",
      "loop no 10.0 for tract no 1662 with population 771498.2374118653\n",
      "loop no 11.0 for tract no 1662 with population 780644.3363088474\n",
      "loop no 9.0 for tract no 1663 with population 781212.6350094269\n",
      "tract,wide, thin angles are  1665 108.6583 71.3417\n",
      "loop no 9.0 for tract no 1666 with population 783111.0309718695\n",
      "loop no 9.0 for tract no 1667 with population 784488.8726422897\n",
      "I am working on tract number 1680 of 3168 tracts\n",
      "loop no 9.0 for tract no 1689 with population 782770.093481264\n",
      "I am working on tract number 1700 of 3168 tracts\n",
      "I am working on tract number 1720 of 3168 tracts\n",
      "loop no 9.0 for tract no 1736 with population 777262.4662474054\n",
      "loop no 10.0 for tract no 1736 with population 783127.4189532459\n",
      "tract,wide, thin angles are  1737 100.1142 79.8858\n",
      "loop no 9.0 for tract no 1737 with population 779910.5272846846\n",
      "loop no 9.0 for tract no 1738 with population 784141.6751930874\n",
      "I am working on tract number 1740 of 3168 tracts\n",
      "tract,wide, thin angles are  1746 129.6491 50.3509\n",
      "loop no 9.0 for tract no 1746 with population 789467.3398122279\n",
      "I am working on tract number 1760 of 3168 tracts\n",
      "tract,wide, thin angles are  1764 94.9234 85.0766\n",
      "loop no 9.0 for tract no 1764 with population 762587.7287349724\n",
      "loop no 10.0 for tract no 1764 with population 786085.9709852019\n",
      "tract,wide, thin angles are  1773 144.0 36.0\n",
      "loop no 9.0 for tract no 1775 with population 780490.1598934111\n",
      "I am working on tract number 1780 of 3168 tracts\n",
      "loop no 9.0 for tract no 1780 with population 825464.4286394195\n",
      "loop no 10.0 for tract no 1780 with population 793563.0470233093\n",
      "tract,wide, thin angles are  1782 102.3885 77.6115\n",
      "loop no 9.0 for tract no 1782 with population 736582.0086099998\n",
      "loop no 10.0 for tract no 1782 with population 776331.7751337416\n",
      "loop no 11.0 for tract no 1782 with population 782128.1520332\n",
      "loop no 9.0 for tract no 1783 with population 395819.9988399598\n",
      "loop no 10.0 for tract no 1783 with population 732791.5240493316\n",
      "loop no 11.0 for tract no 1783 with population 762135.9456252574\n",
      "loop no 12.0 for tract no 1783 with population 751562.600979746\n",
      "loop no 13.0 for tract no 1783 with population 811946.4300964422\n",
      "loop no 14.0 for tract no 1783 with population 804380.7271405174\n",
      "loop no 15.0 for tract no 1783 with population 787956.3732463068\n",
      "we have 2 non-opposing shorted wedges for tract no 1795\n",
      "we have 2 non-opposing shorted wedges for tract no 1796\n",
      "I am working on tract number 1800 of 3168 tracts\n",
      "loop no 9.0 for tract no 1814 with population 772474.9341933313\n",
      "loop no 10.0 for tract no 1814 with population 781288.2760125318\n",
      "tract,wide, thin angles are  1818 84.5613 95.4387\n",
      "loop no 9.0 for tract no 1818 with population 759315.8275568562\n",
      "loop no 10.0 for tract no 1818 with population 688375.7898600199\n",
      "loop no 11.0 for tract no 1818 with population 775279.6694841918\n",
      "loop no 12.0 for tract no 1818 with population 780090.022476797\n",
      "loop no 9.0 for tract no 1819 with population 678283.8783794411\n",
      "loop no 10.0 for tract no 1819 with population 748253.7920119268\n",
      "loop no 11.0 for tract no 1819 with population 772138.2862735204\n",
      "loop no 12.0 for tract no 1819 with population 780788.621296989\n",
      "I am working on tract number 1820 of 3168 tracts\n",
      "loop no 9.0 for tract no 1821 with population 721770.7595191448\n",
      "loop no 10.0 for tract no 1821 with population 753025.2421313041\n",
      "loop no 11.0 for tract no 1821 with population 774262.402666307\n",
      "loop no 12.0 for tract no 1821 with population 781004.052024774\n",
      "loop no 9.0 for tract no 1823 with population 788221.0938259409\n",
      "loop no 9.0 for tract no 1825 with population 788314.7494368905\n",
      "loop no 9.0 for tract no 1826 with population 785705.0429664222\n",
      "tract,wide, thin angles are  1827 140.932 39.068\n",
      "loop no 9.0 for tract no 1827 with population 798497.453680953\n",
      "loop no 10.0 for tract no 1827 with population 786852.802354041\n",
      "loop no 9.0 for tract no 1839 with population 793687.2689360263\n",
      "I am working on tract number 1840 of 3168 tracts\n",
      "loop no 9.0 for tract no 1840 with population 798234.181523524\n",
      "loop no 10.0 for tract no 1840 with population 772688.2578778144\n",
      "loop no 11.0 for tract no 1840 with population 783957.6161944693\n",
      "I am working on tract number 1860 of 3168 tracts\n",
      "loop no 9.0 for tract no 1864 with population 788172.2878430439\n",
      "loop no 9.0 for tract no 1868 with population 786576.1189641098\n",
      "loop no 9.0 for tract no 1869 with population 750527.8173256293\n",
      "loop no 10.0 for tract no 1869 with population 767834.1056376052\n",
      "loop no 11.0 for tract no 1869 with population 785320.0712613924\n",
      "loop no 9.0 for tract no 1870 with population 749266.0939808133\n",
      "loop no 10.0 for tract no 1870 with population 792135.2904256587\n",
      "loop no 9.0 for tract no 1871 with population 782379.239915631\n",
      "tract,wide, thin angles are  1872 132.7311 47.2689\n",
      "loop no 9.0 for tract no 1872 with population 880237.0531267094\n",
      "loop no 10.0 for tract no 1872 with population 795669.147490838\n",
      "loop no 11.0 for tract no 1872 with population 775269.0546147764\n",
      "loop no 12.0 for tract no 1872 with population 783788.5483432794\n",
      "loop no 9.0 for tract no 1873 with population 917946.2887928423\n",
      "loop no 10.0 for tract no 1873 with population 819618.4291193909\n",
      "loop no 11.0 for tract no 1873 with population 801856.3709266796\n",
      "loop no 12.0 for tract no 1873 with population 791446.6794832691\n",
      "loop no 9.0 for tract no 1874 with population 776240.8912674155\n",
      "loop no 10.0 for tract no 1874 with population 781187.4712038815\n",
      "loop no 9.0 for tract no 1875 with population 770744.0131959082\n",
      "loop no 10.0 for tract no 1875 with population 782877.0552707904\n",
      "loop no 9.0 for tract no 1876 with population 771911.1588750348\n",
      "loop no 10.0 for tract no 1876 with population 781102.0205129965\n",
      "I am working on tract number 1880 of 3168 tracts\n",
      "loop no 9.0 for tract no 1894 with population 349260.8263889096\n",
      "loop no 10.0 for tract no 1894 with population 463177.29936687916\n",
      "loop no 11.0 for tract no 1894 with population 554313.2092009645\n",
      "loop no 12.0 for tract no 1894 with population 664641.1905058232\n",
      "loop no 13.0 for tract no 1894 with population 699202.2302446712\n",
      "loop no 14.0 for tract no 1894 with population 733332.356227529\n",
      "loop no 15.0 for tract no 1894 with population 775953.7157258268\n",
      "loop no 16.0 for tract no 1894 with population 778636.4778248442\n",
      "loop no 17.0 for tract no 1894 with population 783139.4129631631\n",
      "I am working on tract number 1900 of 3168 tracts\n",
      "loop no 9.0 for tract no 1900 with population 788870.5398657097\n",
      "tract,wide, thin angles are  1908 120.1323 59.8677\n",
      "loop no 9.0 for tract no 1908 with population 783266.7537148267\n",
      "loop no 9.0 for tract no 1909 with population 785945.4914021024\n",
      "loop no 9.0 for tract no 1910 with population 730542.191975136\n",
      "loop no 10.0 for tract no 1910 with population 770718.2328904307\n",
      "loop no 11.0 for tract no 1910 with population 780686.2087046339\n",
      "loop no 9.0 for tract no 1911 with population 770890.340528624\n",
      "loop no 10.0 for tract no 1911 with population 781597.8025966235\n",
      "loop no 9.0 for tract no 1913 with population 779445.9990655312\n",
      "loop no 9.0 for tract no 1915 with population 784452.4240622596\n",
      "loop no 9.0 for tract no 1916 with population 770201.3577219798\n",
      "loop no 10.0 for tract no 1916 with population 779128.3198209751\n",
      "tract,wide, thin angles are  1917 135.9966 44.0034\n",
      "loop no 9.0 for tract no 1917 with population 787286.457291257\n",
      "loop no 9.0 for tract no 1918 with population 792689.898883069\n",
      "loop no 9.0 for tract no 1919 with population 753950.0831868299\n",
      "loop no 10.0 for tract no 1919 with population 777585.7172079157\n",
      "loop no 11.0 for tract no 1919 with population 783582.9029563799\n",
      "I am working on tract number 1920 of 3168 tracts\n",
      "I am working on tract number 1940 of 3168 tracts\n",
      "loop no 9.0 for tract no 1955 with population 781324.5377767868\n",
      "I am working on tract number 1960 of 3168 tracts\n",
      "loop no 9.0 for tract no 1972 with population 736497.186502874\n",
      "loop no 10.0 for tract no 1972 with population 781917.8050483846\n",
      "I am working on tract number 1980 of 3168 tracts\n",
      "loop no 9.0 for tract no 1981 with population 754753.5396146894\n",
      "loop no 10.0 for tract no 1981 with population 769016.9052222451\n",
      "loop no 11.0 for tract no 1981 with population 779928.3441755599\n",
      "loop no 9.0 for tract no 1982 with population 782250.5976380812\n",
      "loop no 9.0 for tract no 1986 with population 787960.1348974451\n",
      "tract,wide, thin angles are  1989 92.2319 87.7681\n",
      "loop no 9.0 for tract no 1989 with population 770403.2468807548\n",
      "loop no 10.0 for tract no 1989 with population 782282.8383266253\n",
      "loop no 9.0 for tract no 1991 with population 773922.5440688368\n",
      "loop no 10.0 for tract no 1991 with population 778723.2784180169\n",
      "loop no 11.0 for tract no 1991 with population 783271.9383515114\n",
      "loop no 9.0 for tract no 1994 with population 731866.7416436218\n",
      "loop no 10.0 for tract no 1994 with population 788563.9996044501\n",
      "loop no 9.0 for tract no 1995 with population 769325.6224341466\n",
      "loop no 10.0 for tract no 1995 with population 782938.5143299414\n",
      "I am working on tract number 2000 of 3168 tracts\n",
      "loop no 9.0 for tract no 2004 with population 783161.3835323447\n",
      "tract,wide, thin angles are  2007 131.1453 48.8547\n",
      "loop no 9.0 for tract no 2007 with population 596404.6981710499\n",
      "loop no 10.0 for tract no 2007 with population 632072.39346624\n",
      "loop no 11.0 for tract no 2007 with population 715662.6126786419\n",
      "loop no 12.0 for tract no 2007 with population 752285.3688780431\n",
      "loop no 13.0 for tract no 2007 with population 769734.345884098\n",
      "loop no 14.0 for tract no 2007 with population 777650.1350383515\n",
      "loop no 15.0 for tract no 2007 with population 781451.18335098\n",
      "loop no 9.0 for tract no 2009 with population 777264.3798069032\n",
      "loop no 10.0 for tract no 2009 with population 784852.3952638283\n",
      "loop no 9.0 for tract no 2013 with population 730909.2976119501\n",
      "loop no 10.0 for tract no 2013 with population 757809.3230590099\n",
      "loop no 11.0 for tract no 2013 with population 767625.3563132464\n",
      "loop no 12.0 for tract no 2013 with population 776946.1450403186\n",
      "loop no 13.0 for tract no 2013 with population 780586.4120088432\n",
      "loop no 9.0 for tract no 2014 with population 783067.0892403332\n",
      "tract,wide, thin angles are  2016 90.7449 89.2551\n",
      "loop no 9.0 for tract no 2016 with population 689717.6618458433\n",
      "loop no 10.0 for tract no 2016 with population 734556.1794886384\n",
      "loop no 11.0 for tract no 2016 with population 770165.7212332805\n",
      "loop no 12.0 for tract no 2016 with population 779891.2276470338\n",
      "I am working on tract number 2020 of 3168 tracts\n",
      "loop no 9.0 for tract no 2027 with population 750434.617131613\n",
      "loop no 10.0 for tract no 2027 with population 778468.5282605015\n",
      "loop no 11.0 for tract no 2027 with population 783065.9542260501\n",
      "loop no 9.0 for tract no 2028 with population 781779.1814742422\n",
      "I am working on tract number 2040 of 3168 tracts\n",
      "tract,wide, thin angles are  2043 123.6092 56.3908\n",
      "loop no 9.0 for tract no 2043 with population 838240.6664116213\n",
      "loop no 10.0 for tract no 2043 with population 838319.5136544231\n",
      "loop no 11.0 for tract no 2043 with population 838339.4101627637\n",
      "loop no 12.0 for tract no 2043 with population 838345.7151306544\n",
      "loop no 13.0 for tract no 2043 with population 838348.0804003149\n",
      "loop no 14.0 for tract no 2043 with population 838349.0918269266\n",
      "loop no 15.0 for tract no 2043 with population 838349.5721030154\n",
      "loop no 16.0 for tract no 2043 with population 838349.8318553802\n",
      "loop no 17.0 for tract no 2043 with population 838349.9877069269\n",
      "loop no 18.0 for tract no 2043 with population 838350.0812179579\n",
      "loop no 19.0 for tract no 2043 with population 838350.1180767405\n",
      "loop no 20.0 for tract no 2043 with population 838350.1401920267\n",
      "loop no 21.0 for tract no 2043 with population 838350.1534612045\n",
      "loop no 22.0 for tract no 2043 with population 838350.1614227135\n",
      "loop no 23.0 for tract no 2043 with population 838350.166199623\n",
      "loop no 24.0 for tract no 2043 with population 838350.1690657645\n",
      "loop no 25.0 for tract no 2043 with population 838350.1707854558\n",
      "loop no 26.0 for tract no 2043 with population 838350.1718172682\n",
      "loop no 27.0 for tract no 2043 with population 838350.1724363525\n",
      "loop no 28.0 for tract no 2043 with population 838350.1726663134\n",
      "loop no 29.0 for tract no 2043 with population 838350.1727305811\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 0.44576 0.2433 111664.7223 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 4.19545 0.2433 276708.7363 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.24524 0.2433 224988.2852 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.85057 0.2433 224988.4289 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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3nuLI+rttM/9vT330+X6v1L7SsaByoX+42eRtV/4mAD/94hdx4fnnkO027XbSzjaZ0G63yczOv3Qet9tJ9mxvd7dnFvt2j8/5fTv/5vy+7XYCxb7Fc2QmWRzbu3/2bF84X0Imc3NzNJutnuN766FTR3tp/e1sQ7G9Pb+9OP+S+hf+f8v9X7v7aTArdqo9ywudR8++fTpVegcJPYOAfp1qLO60otHTSR7ZqcUgnWwjGKzT6/9/XdLJ9ta/aJDQ+f8fOchpFIOcWEf9gw2KFrX3Wgdhi+pb+v1eOkiYmGhw/JYtG/paXUmlQr/VmuPtV32AO//gXn7ryn/Hv3jDqze6pGNCb8e0UqeXvZ1YO8meDnC5DmX15y6W55/vyA582Q6xp4PrbmdRB73QIR55/u5zHNGhZ6dDXlz/wvkW/V8W1d/toFccgPR02Nmn/uXat+/3oljfbB7dAGRJ/au19wADKC24+LX/hA+9953Fb5TjpTKh3263ufw3ruO2O7/Br1+228AfoohgYmKCiY0uRJW2lt+al+67uIPs7tvtUAb7DXvlAUi3g14YYOSi83c7yd4Oun+HmPOdZ/c37m59f/rod/nvv3s72054Dle9Y/dGf1uWqEToZybv2XMjn/+fd3LFpZfwtje/YaNLkrTIwvQHUOMhRGZy3NQUN3/ic0w/70QuHbO8qkTo/9fP38Ynv/BlAG797Jf4xOdv65n3XDTvN9DF2LXOG3bnARdfXOw3D7jaxcWe5zlinnaF+pdcjB2wfi+wSiMXEVz7rkt58qkD/Ob1tzB94jYuPP+cjS5rXiVC/8yf+WkueeNrmJtrL3Mxlp5f3RbmHvvN/eaiX8V6f1Wcm5vrOxfqBdbx5gVWL7Ae2QbddlnaBssPTIY7eJiYmOCGa6/grw88zeXXXseJ207gF87u+3emRi5GGS4zMzM5Ozs7svNV3aAXWBfuKmovdG69nUvfi4irdZw5f4H1yI6zPX8XUjuXdpx951iXXKTtXjRd6SLtCnOsvZ3qAHOsR3+Refn6j5wj7jfQWMcc8aL2XnLReYWL1P3q12CO6MxW+o18UcfXaETPb9NHdnyHDh/m+z94ki2bj+Prn/sof+cFR/enxCNib2bODOP/WYmRfl15gVXDsOqFzVUGCd2OY/GdVctdeM0+g4S63521+bgpjt+yeWNfCAVDXzrGeYFVvcbvJlJJUmkMfUmqEUNfkmrE0JekGjH0JalGDH1JqhFDX5JqxNCXpBoZ6ccwRMR+4C9LPMVJwJMlPv96jGtt41oXjG9t1rV241rbuNYFR9b2k5k5PYwnHWnoly0iZof1+RTDNq61jWtdML61WdfajWtt41oXlFeb0zuSVCOGviTVyLEW+rdsdAErGNfaxrUuGN/arGvtxrW2ca0LSqrtmJrTlySt7Fgb6UuSVmDoS1KNVC70I+L0iPjDiLg/ImYj4qyebadFxD0R8VBEfDMilvypmoi4JiIeL46/PyLOH6PaToyIOyLikeLf55ZZV0TsjIhne9ri5mWOL6XNhlBXKe21Um0920+JiIMR8e5ljh9pm62hrlG/xs7qaYN9EfH6ZY4f+c/lGmobdZudGxF7i5zYGxG7ljn+6Nqs+6fUqvIF/C/gl4rl84G7iuVJ4AHgZcXj5wETfY6/Bnj3mNb2QeDKYvlK4AMl17UTeHCA40tpsyHUVUp7rVRbz/bPA59drl1G3WZrqGvUr7HjgclieTvwRPfxKNprSLWNus3OAF5YLJ8KPD7M11jlRvpAAluL5ROA7xXLvwg8kJn7ADLzh5k5V7HaXgfcWizfClxQcl0bbb11ldVesEJtEXEB8OfAQ0M836DWW9dIX2OZ+Uxmtor1m4v9Rm29tY26zf44M7vf14eAzRFx3JDOWcmR/kuB7wDfBR6n8/ZkgMuATwBfAf4P8Ksr9I6P0hl5fwx47hjVdmDR46dKrmsn8CPgj4GvA68YZZsNoa5S2muV2v4WcA/wt1lhpLUBbTZoXSN9jRXbfpZOeB0EXj/K9hpSbSNvs5593gDcOcw2G0qjDvsLuBN4sM/X64DfBi4s9ruo2yDAu4G/oPN5FccXPwCv6vPcz6fz16EbwPuAj41RbUf94jrKuo4Dnlcsn1m8+LYOs81KrmtdP4xHWduHgYt6fuiWC9dRt9mgdY30Nbbo+JcC9wGbx+Hncg21bUibAf8A+DPg7w7zNTZwo47LF/A0C+8vCOBviuWLgd/p2e8q4IpVnmsnA8wdj6o24NvA9mJ5O/DtMuvqs99dwMyo2my9dZXVXqt8L/+AzujqUeAA8NfAv93oNhu0rjF4jX1tlK+xYdS2EW0G7AD+BHj5gM81cJtVcU7/e8DPF8u7gEeK5a8Ap0XE8RExWezzfxcfHBHbex6+nk6vOxa1AV8ELimWLwF+r8y6ImI6IiaK5RcBf5/OnPARSmyzddVFee21bG2Z+YrM3JmZO4Hrgfdn5n9cfPCo22zQuhj9a+ynitc8EfGTwEvodExH2Iify0FrY/Rttg34MvCezPzfyx181G02rN50VF/APwL2AvuAe4Eze7a9mc783IPAB3vWf4SiB6czt/5NOvNgX6TowcektucBXy2++V8FTiyzLuDCoqZ9dK41vGaUbTaEukppr9W+lz37XEPPNMpGttka6hr1a+wtxffy/uJ7ecEo22tItY26zd5L55rW/T1fJw+rzfwYBkmqkSpO70iSjpKhL0k1YuhLUo0Y+pJUI4a+JNWIoS9JNWLoS1KN/H/o9qBUlo37zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop no 30.0 for tract no 2043 with population 838350.1727468903\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 0 0.44576 0.2433 111664.7223 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 1 4.06245 0.2433 276708.7363 0\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 2 0.24524 0.2433 224988.2852 1\n",
      "wedge no, currentR,guessedR, wedgePop, overEdge? 3 0.85057 0.2433 224988.429 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWtklEQVR4nO3de5QcZZnH8d/TPZPMIISEpEMiMYyCAitykTHuQURODJhVRNEjF8FFwY0Cyv0qNxNuCRBDBCJmD2A4ulxEV1cEFZAIrHKZuEmWaBAvsGtCnCES2AGSme5+9o+ununu6Z7pSaa78vZ8P+f0Od1V1VVPv+n83prqqrfM3QUACE8i7gIAAFuHAAeAQBHgABAoAhwAAkWAA0Cgmuq5sUmTJnlbW1s9NwkAwVuxYsXL7p4qnV7XAG9ra1NHR0c9NwkAwTOzF8tN5xAKAASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBqvo8cDNLSuqQtM7djzSzXSTdI6lN0guSjnH3V2pRJEYvd9fS763XgiUvqqfXlUyYkkkpmTQ1JU3J/CMRTWsyJRKmZCKa3xTNS+SeN5Us37eehCmRVPl19q3HCtaTW2cikdtmU9KUKFlnU9KUiNYzWK35ebtNGavdpoyNu8kRkOFcyHOmpN9LGhe9vkjSI+4+38wuil5fOML1YZTbvCWreTe+0Pc6nXGlM5LUmOPYr+v4QNwlICBVBbiZTZP0MUlXSzonmvwJSYdFz5dJWi4CHCOstSXZF2rZrCuTdWUyUibjSmdcmfwjmp5O555ns650umCZrPqWTWdc2YyUyfbP739ftI6+dUbrHbCt/HtzdaUL1pvOFG8/my1ZR7Z/ufz8Z597XUccukvMrY3QVLsHfqOkCyTtVDBtV3d/SZLc/SUzm1zujWY2R9IcSZo+ffrWV4pRLxEdsmiu6wAQwPZryB8xzexISZ3uvmJrNuDuS9293d3bU6kBY7EAALZSNfsyH5B0lJl9VFKLpHFm9l1JfzOzqdHe91RJnbUsFABQbMg9cHe/2N2nuXubpOMk/dLdT5T0H5JOihY7SdKPa1YlAGCAbTkPfL6kw83seUmHR68BAHUyrJ+D3H25cmebyN03SvrwyJcEAKgGV2ICQKAIcARv85bNuumOBfr7ppfjLgWoKwIcwVv9+xWaf8tl+uezPqk33nw97nKAuiHAEbwtPVskSf/17NM69WsnKJ1Ox1wRUB8EOIKXTvdKkj5z5Of08OMP6KJrT5d7Y46VAhTiomQErzcK8C8cc5qmTt5N37x9vqZMfqvO+9IVMVcG1BZ74Ahefg+8ublZF5w6V8d+/CQt+terNfOYA+ItDKgxAhzB6+3tkSQ1NzXLzLTgkiWSpOf+/DsOpaChcQgFwcsfQmlqapaUC/J99txXE8ZPkpnFWRpQU+yBI3j5s06aowCXcmHe2rJDXCUBdUGAI3ile+BS7rh4cxN/YKKxEeAIXv4Y+JjmMf3T0r1FgQ40IgIcwSu3B97b21sU6EAjIsARvL7TCAsDvMZ74NksZ7cgfgQ4glf5GHhtAnzuooc189ilNVk3MBz8yoPgpTNRgCf7v8613ANf+r2na7JeYLjYA0fwent7+i7i6ZuW7uFHTDQ8AhzB602nB4R1Ot2rMQQ4GhwBjuCVO97NaYQYDQhwBK80rN1dmUymZj9iAtsLAhzBS6d7B1zEI4k9cDQ8AhzB6+ntKbmIp390QqCREeAIXrrkEEpv3/jgXImJxkaAI3i9JQNXlbsyE2hEBDiCV2kPnGPgaHQEOILXm04X/YjJHjhGCwIcwest+RGzJ/oRs4nxwNHgCHAEr/RCnjQ/YmKUIMARvNILeXo5hIJRggBH8AbugefukcmPmGh0BDiC11sS4FzIg9GCAEfwSoeO5TRCjBYEOIJXugee/xGzFvfEfK1784ivE9haQ55nZWYtkh6TNDZa/j53v8LM9pd0q6QdJb0g6QR3f62GtQJlpUvGA6/lHvi4HVt05Ky9dcSh7xrxdQPDVc2JslskzXT3bjNrlvSEmT0o6SZJ57n7r8zsZEnnS7qshrUCZVXaA6/VMfBvz/9UTdYLDNeQh1A8pzt62Rw9XNJeyu2ZS9JDkj5dkwqBIaTTvUXnfPekuZAHo0NVx8DNLGlmKyV1SnrI3Z+S9Kyko6JFPiPpbRXeO8fMOsyso6urawRKBorl74mZx6X0GC2qCnB3z7j7AZKmSZphZvtKOlnS6Wa2QtJOknoqvHepu7e7e3sqlRqhsoF+lS/k4UpMNLZhnYXi7pskLZc0293XuvsR7n6QpLsk/WnkywOGlh4wnCwX8mB0GDLAzSxlZuOj562SZklaa2aTo2kJSZcqd0YKUHele+AbOtdJ4hAKGl81e+BTJT1qZqslPaPcMfD7JR1vZn+QtFbSekl31K5MoLxMJiN37/sR82fLf6wbb7tGh8yYqdTEXWOuDqitIX+md/fVkg4sM32xpMW1KAqoVk/BZfOPPfmwTr34BO23z0G67frvy8xirg6oLa7ERNDyZ5ysWtOhL5z7ae3Rtpe++82faMe37BRzZUDtEeAIWv6Mkwce/ZHeuus03XXzAxo/bkLMVQH1QYAjaNlsRpI0beruunvJzzjujVGFS9UQtIkTUrri7Os1+7CjtNuUsteSAQ2LAEfQzExzTjgz7jKAWHAIBQACRYADQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKAIcAAI1JABbmYtZva0ma0yszVmNjeafoCZPWlmK82sw8xm1L5cAEBeUxXLbJE00927zaxZ0hNm9qCkeZLmuvuDZvZRSddJOqx2pQIACg0Z4O7ukrqjl83Rw6PHuGj6zpLW16JAAEB51eyBy8ySklZI2lPSLe7+lJmdJennZnaDcodiDq7w3jmS5kjS9OnTR6JmAICq/BHT3TPufoCkaZJmmNm+kk6VdLa7v03S2ZJuq/Depe7e7u7tqVRqhMoGAAzrLBR33yRpuaTZkk6S9MNo1vcl8SMmANRRNWehpMxsfPS8VdIsSWuVO+b9oWixmZKer1GNAIAyqjkGPlXSsug4eELSve5+v5ltkrTYzJokbVZ0nBsAUB/VnIWyWtKBZaY/IemgWhQFABgaV2ICQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgmoZawMxaJD0maWy0/H3ufoWZ3SNpr2ix8ZI2ufsBNaoTAFBiyACXtEXSTHfvNrNmSU+Y2YPufmx+ATNbKOnVWhUJABhoyAB3d5fUHb1sjh6en29mJukYSTNrUSAAoLyqjoGbWdLMVkrqlPSQuz9VMPuDkv7m7s9XeO8cM+sws46urq5tLhgAkFNVgLt7Jjq+PU3SDDPbt2D28ZLuGuS9S9293d3bU6nUNhULAOg3rLNQ3H2TpOWSZkuSmTVJ+pSke0a6MADA4IYMcDNLmdn46HmrpFmS1kazZ0la6+5/rVmFAICyqjkLZaqkZWaWVC7w73X3+6N5x2mQwycAgNqp5iyU1ZIOrDDv8yNdEACgOlyJCQCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKAIcAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAjUkAFuZi1m9rSZrTKzNWY2t2DeV83suWj6dbUtFQBQqKmKZbZImunu3WbWLOkJM3tQUqukT0jaz923mNnkWhYKACg2ZIC7u0vqjl42Rw+XdKqk+e6+JVqus1ZFAgAGquoYuJklzWylpE5JD7n7U5LeJemDZvaUmf3KzN5XwzoBACWqCnB3z7j7AZKmSZphZvsqt/c+QdI/Sjpf0r1mZqXvNbM5ZtZhZh1dXV0jVzkAjHLDOgvF3TdJWi5ptqS/Svqh5zwtKStpUpn3LHX3dndvT6VS214xAEBSdWehpMxsfPS8VdIsSWsl/UjSzGj6uySNkfRyrQoFABSr5iyUqZKWmVlSucC/193vN7Mxkm43s2cl9Ug6KfrBEwBQB9WchbJa0oFlpvdIOrEWRQEAhsaVmAAQKAIcAAJFgANAoAhwAAhUNWehBG1LT4+eWbVGe+/RJjOTWUKJhCmRSMgkJRK512YJmUWvLZo/8LokANhuNHyAf+nCq/TQ409u9fvNLBf4lg96i6blgt4Kwr6/Y8g9t+h9heuwhPXN71tHIuo88p1L1JlYUWeivvcXb7u/Uypcb2md/Z3UIJ+nYB3ltl2po1NJ7ZXXG9Vf2GbWv60B7WT9y+ana0Anq/5OuUxbF267ckddvI7B2mVr2haolYYO8Hvv/0VfeM8991QlEwm5u7LuymazyrpL7spmXVnPKpv1aH5Wnu1fLv8ez0avFb0nP6/gfbnnWbmrbxu55SSP5uene36dRdt2edH78nXk1pvNZNXbm+6br8LPE9Wsgs+X33b/ZyquzQtqybeDF9SSX2+5z4TqVOpwC/8KtIKOe9COvbRji9ZrRZ2JDd3Zl6xDJZ3PkJ1lwY7KwPWW39nJf84BOzDD2Iko1065nQjr+/zFn6lCu5Rp68oddf9n2muPNo0dMybur1Sfhg3wnz7yuM6d9w0dMuNALVt0pVrGbj+N3ii8NPgLO5qoM/HCjqigMynsAAvXke/I3Is7uqL1FnSiXrLt/DqKOra+zq5k28p1SIN1okV1lHTU1dY/oAMv04lW3y6V2zab9f52Kfk8fZ8zm1EmM8i2o9oKdyLy6x3YTvl1lv77l+58VP48oXnP3nvqvm/foB3fskPcpUhq0AB/9NfP6PRLrtV7991bdyz8OuFdI/17KJKUjLscBGjQjq6gk63YKQ3oLPN/HfY/H+yv3Uo7F170l3bufes2dOryG5ZozoVX6juL5mlMc3Pczdd4Af7kb1fri+fP1V57tOnOxVdph9bWuEsCUIGZKZlMBtP9t44dq3PmLdR5Vy7S4rnnx/4bR0MF+Mo1z+mksy/XtKm76t9uvkY777Rj3CUBaCDHHvURvdT5sq6/dZmmpCbqa189JdZ6GibA1/7xLzrhjK9pl/HjdPeSBZo4YXzcJQFoQGee8llt6NqoW5bdoympiTr5uE/GVktDBPif/2edjjv9IrWMGaO7b1mgqZMHDEsOACPCzHT1Baera+PfdfnCbyk1aRd9fNahsdQS/JWY6zZ06rjTLlQmk9XdSxZo92lT4y4JQINLJpO6+aqL1b7fP+iMyxboNytWx1JH0AHetfEVHXvahfq/7td1183X6p1vnx53SQBGidaWsbrjG3O1+7SpOvncK/T7P/6l7jUEG+CvvPqajj/9Im3ofFl3Lr5K++69Z9wlARhlJuw8Tt+76Rrt0NqiE8+4ROs2dNZ1+0EGePfrb+hzZ1yiP734V92+cK7et/+74y4JwCi125TJ+u5N1+j119/QiWdcoldefa1u2w4uwN/cvEWfP+cKrV77vG699hId+v73xl0SgFFunz3frtsXztUL/7teJ5/7db25eUtdthtUgPf09mrOhVfqyd+u1uK5F+gjhx0cd0kAIEk6uH1/LZ53gZ5ZtUZfufRaZTKZmm8zmABPpzP66mUL9Mv/fFrzLz5DR8+eGXdJAFDkqMM/pLnnflk/W/5rXXr9LTUf7yWI88Cz2azOv3qR7n/4MV1+1hyd+KmPxV0SAJR1ynFHa0PnRi25815NSU3Smad8tmbbCiLA77zvft37k19Ikr55x126+Tv3FA3xqMKhMaMhIS1RPKxl4XCW5YftLBiO0hIFw2uWrnfrh+0sNzylldxYIj+mdflxs0s/01YM21mmXQYbd7x0yNDS+isNO1q8juIbZ1Rul/62TSQSUpn6B1sHsL24+Csna0PXRl33re9oSmqijj3qIzXZThAB/v73vkdfPP7ogjGv+4fjLDuSWNHwov1DaeZGFysejrPcyGf5YTfT6UrDixYOpVk8YlrWyw1b6kXDblYeMrXS+OLhDbsZh2puPlHUiQ7asW3bzSequXFEfv7AG4SUH187kTCp4OYUZXdghhp3vKSTrbgTsY3jjldcR+kY59FOQv7mG4U35ehvq9Ibpwxch0rqr3zzldJ2saJlR/K7uPDyc7TxlU06/+pFmrTLBH34kBkjtv48q2c4tLe3e0dHR9221yjcCwK/YIzmckNmFo/HXa6j86JhN8sNx6nCMa/LrKNo3OlBbwjR31mWu/lEf8dWoXMczrjZQ663eMzw0rYaqm0HjCdeZtzsATffKDMe94CdiJJxs6u/0UfBTTrKDLta2G7cfKM6fYFf0BGX/hVY7g5UNsjds3p6erX+b11qbRmrX913m3abMnlra1vh7u2l04PYAx/tCvcOQhl2E9uXam4QMaBDL+ioK9+UYei7T5XefKOaO1iV/hU73DtYVbp7Vmn9fX8xD+PmG9W1S3Hbtowdox1aW0b835UAB0YBbr7RmII5jRAAUIwAB4BAEeAAECgCHAACRYADQKAIcAAIFAEOAIEiwAEgUHW9lN7MuiS9WLcNhmOSpJfjLmI7RvtURtsMrlHaZ3d3T5VOrGuAozwz6yg3zgFyaJ/KaJvBNXr7cAgFAAJFgANAoAjw7cPSuAvYztE+ldE2g2vo9uEYOAAEij1wAAgUAQ4AgSLAY2RmB5jZk2a20sw6zGxGwbz9zOw3ZrbGzP7bzEb+dh7bsUptY2ZtZvZmNH2lmd0ad61xGOy7E82fbmbdZnZeXDXGZZDvzoyC780qMzs67lq3WeH9FnnU9yHpF5L+KXr+UUnLo+dNklZL2j96PVFSMu56t5O2aZP0bNz1xf2o1D4F838g6fuSzou71u2lbSTtIKkpej5VUmf+dagPbqkWL5c0Lnq+s6T10fMjJK1291WS5O4bY6gtbpXaBjkV28fMPinpz5Jer39Z24WybePubxQs0xItFzTOQomRme0j6eeSTLnDWQe7+4tmdpakgyRNlpSSdLe7XxdboTEYpG3aJK2R9AdJr0m61N0fj63QmAzSPm+R9LCkwyWdJ6nb3W+Ir9L6q9Q20bz3S7pd0u6SPufu/x5boSOAPfAaM7OHJU0pM+sSSR+WdLa7/8DMjpF0m6RZyv27HCLpfZLekPSIma1w90fqVHZdbGXbvCRpurtvNLODJP3IzN7t7q/VrfA62cr2mStpkbt3m1n9iq2zrWwbuftTkt4dhfwyM3vQ3TfXq+6Rxh54jMzsVUnj3d0t97/tVXcfZ2bHSZrt7p+PlrtM0mZ3vz7GcuuqUtuUWW65csd5O+pdY5wG+e48Lult0WLjJWUlXe7uN8dUat0N47vzqKTzQ/7ucBZKvNZL+lD0fKak56PnP5e0n5ntYGZN0TK/i6G+OJVtGzNLmVkyev4OSe9U7njvaFO2fdz9g+7e5u5tkm6UdM1oCu9Ipe/O26P/TzKz3SXtJemFOAocKRxCide/SFocfak2S5ojSe7+ipl9Q9Izyv3Q8oC7/zS+MmNRtm0kHSppnpmlJWUkfdnd/x5TjXGq1D6o3DaHSLrIzHqV+8vkNHcPeqhZDqEAQKA4hAIAgSLAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKD+HzzGgaGJ64TqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 30 times on tract 2043, giving up w pop 838350.1727468903\n",
      "loop no 9.0 for tract no 2044 with population 784449.9301797824\n",
      "loop no 9.0 for tract no 2051 with population 699128.7630673675\n",
      "loop no 10.0 for tract no 2051 with population 773794.5977339465\n",
      "loop no 11.0 for tract no 2051 with population 780040.3111732248\n",
      "tract,wide, thin angles are  2052 142.6264 37.3736\n",
      "loop no 9.0 for tract no 2052 with population 782263.7567115654\n",
      "loop no 9.0 for tract no 2053 with population 782331.6429555234\n",
      "loop no 9.0 for tract no 2058 with population 589054.636072174\n",
      "loop no 10.0 for tract no 2058 with population 682079.0165149933\n",
      "loop no 11.0 for tract no 2058 with population 745218.9084072349\n",
      "loop no 12.0 for tract no 2058 with population 764654.9923988725\n",
      "loop no 13.0 for tract no 2058 with population 775337.4764327426\n",
      "loop no 14.0 for tract no 2058 with population 780903.8471193165\n",
      "I am working on tract number 2060 of 3168 tracts\n",
      "loop no 9.0 for tract no 2067 with population 777309.4909797701\n",
      "loop no 10.0 for tract no 2067 with population 783960.8474011862\n",
      "loop no 9.0 for tract no 2068 with population 725756.5274022978\n",
      "loop no 10.0 for tract no 2068 with population 768365.967438671\n",
      "loop no 11.0 for tract no 2068 with population 782940.707487339\n",
      "loop no 9.0 for tract no 2069 with population 776643.0636645685\n",
      "loop no 10.0 for tract no 2069 with population 787145.8903751695\n",
      "tract,wide, thin angles are  2070 113.0171 66.9829\n",
      "loop no 9.0 for tract no 2070 with population 787682.5209110631\n",
      "loop no 9.0 for tract no 2071 with population 785077.8897853529\n",
      "loop no 9.0 for tract no 2074 with population 772902.7182079178\n",
      "loop no 10.0 for tract no 2074 with population 781684.6204459254\n",
      "loop no 9.0 for tract no 2075 with population 782336.1427858552\n",
      "loop no 9.0 for tract no 2076 with population 732315.5298940531\n",
      "loop no 10.0 for tract no 2076 with population 756080.2520242535\n",
      "loop no 11.0 for tract no 2076 with population 774911.1284428047\n",
      "loop no 12.0 for tract no 2076 with population 781462.3816234581\n",
      "loop no 9.0 for tract no 2077 with population 768497.8750287156\n",
      "loop no 10.0 for tract no 2077 with population 781125.0586218472\n",
      "loop no 9.0 for tract no 2078 with population 775684.5003415919\n",
      "loop no 10.0 for tract no 2078 with population 781865.8053865856\n",
      "I am working on tract number 2080 of 3168 tracts\n",
      "loop no 9.0 for tract no 2094 with population 616349.2512905165\n",
      "loop no 10.0 for tract no 2094 with population 693816.4297922455\n",
      "loop no 11.0 for tract no 2094 with population 738283.7298085513\n",
      "loop no 12.0 for tract no 2094 with population 765553.8665168965\n",
      "loop no 13.0 for tract no 2094 with population 774264.9894982334\n",
      "loop no 14.0 for tract no 2094 with population 780192.5698272921\n",
      "loop no 9.0 for tract no 2096 with population 753471.2401164819\n",
      "loop no 10.0 for tract no 2096 with population 769246.9103544822\n",
      "loop no 11.0 for tract no 2096 with population 781317.7739643523\n",
      "I am working on tract number 2100 of 3168 tracts\n",
      "loop no 9.0 for tract no 2101 with population 785925.5841028866\n",
      "loop no 9.0 for tract no 2113 with population 750231.4709643573\n",
      "loop no 10.0 for tract no 2113 with population 779383.1230776496\n",
      "loop no 9.0 for tract no 2114 with population 779065.8847193723\n",
      "tract,wide, thin angles are  2115 101.1835 78.8165\n",
      "loop no 9.0 for tract no 2115 with population 774887.2319124443\n",
      "loop no 10.0 for tract no 2115 with population 782483.0144064748\n",
      "I am working on tract number 2120 of 3168 tracts\n",
      "loop no 9.0 for tract no 2123 with population 791966.7833986853\n",
      "tract,wide, thin angles are  2124 109.5399 70.4601\n",
      "loop no 9.0 for tract no 2125 with population 790928.750364692\n",
      "loop no 9.0 for tract no 2126 with population 775258.1574183792\n",
      "loop no 10.0 for tract no 2126 with population 783978.729448943\n",
      "we have 2 non-opposing shorted wedges for tract no 2127\n",
      "we have 2 non-opposing shorted wedges for tract no 2129\n",
      "loop no 9.0 for tract no 2134 with population 706506.5551666738\n",
      "loop no 10.0 for tract no 2134 with population 792810.1041568092\n",
      "we have 2 non-opposing shorted wedges for tract no 2137\n",
      "I am working on tract number 2140 of 3168 tracts\n",
      "I am working on tract number 2160 of 3168 tracts\n",
      "loop no 9.0 for tract no 2171 with population 784806.8089439126\n",
      "loop no 9.0 for tract no 2173 with population 785329.5096371748\n",
      "tract,wide, thin angles are  2178 113.8927 66.1073\n",
      "we have 2 non-opposing shorted wedges for tract no 2179\n",
      "I am working on tract number 2180 of 3168 tracts\n",
      "loop no 9.0 for tract no 2180 with population 857334.4348337131\n",
      "loop no 10.0 for tract no 2180 with population 799367.570021797\n",
      "loop no 11.0 for tract no 2180 with population 790473.8072513497\n",
      "loop no 9.0 for tract no 2181 with population 697313.0214227766\n",
      "loop no 10.0 for tract no 2181 with population 752265.1354908565\n",
      "loop no 11.0 for tract no 2181 with population 785188.9419986486\n",
      "loop no 9.0 for tract no 2183 with population 643295.2932580875\n",
      "loop no 10.0 for tract no 2183 with population 827199.6179127696\n",
      "loop no 11.0 for tract no 2183 with population 788534.5268449478\n",
      "I am working on tract number 2200 of 3168 tracts\n",
      "tract,wide, thin angles are  2205 121.7079 58.2921\n",
      "loop no 9.0 for tract no 2218 with population 716050.0886214781\n",
      "loop no 10.0 for tract no 2218 with population 756235.9555683734\n",
      "loop no 11.0 for tract no 2218 with population 776001.5715740663\n",
      "loop no 12.0 for tract no 2218 with population 782033.6606616372\n",
      "loop no 9.0 for tract no 2219 with population 764217.0968472763\n",
      "loop no 10.0 for tract no 2219 with population 779231.6989870969\n",
      "I am working on tract number 2220 of 3168 tracts\n",
      "I am working on tract number 2240 of 3168 tracts\n",
      "tract,wide, thin angles are  2241 96.6888 83.3112\n",
      "loop no 9.0 for tract no 2241 with population 755429.4446983992\n",
      "loop no 10.0 for tract no 2241 with population 790960.6294072104\n",
      "I am working on tract number 2260 of 3168 tracts\n",
      "I am working on tract number 2280 of 3168 tracts\n",
      "loop no 9.0 for tract no 2282 with population 747735.7736648521\n",
      "loop no 10.0 for tract no 2282 with population 739877.0691789316\n",
      "loop no 11.0 for tract no 2282 with population 757950.1472892112\n",
      "loop no 12.0 for tract no 2282 with population 774079.4544969823\n",
      "loop no 13.0 for tract no 2282 with population 780344.1562921067\n",
      "loop no 9.0 for tract no 2283 with population 768918.1556247976\n",
      "loop no 10.0 for tract no 2283 with population 783338.9139045333\n",
      "loop no 9.0 for tract no 2284 with population 773925.9700175666\n",
      "loop no 10.0 for tract no 2284 with population 780568.6118085529\n",
      "I am working on tract number 2300 of 3168 tracts\n",
      "I am working on tract number 2320 of 3168 tracts\n",
      "loop no 9.0 for tract no 2339 with population 733528.0210660839\n",
      "loop no 10.0 for tract no 2339 with population 775535.112678161\n",
      "loop no 11.0 for tract no 2339 with population 783629.4959717477\n",
      "I am working on tract number 2340 of 3168 tracts\n",
      "tract,wide, thin angles are  2340 90.2387 89.7613\n",
      "loop no 9.0 for tract no 2340 with population 781567.1023248086\n",
      "loop no 9.0 for tract no 2343 with population 756629.9262160779\n",
      "loop no 10.0 for tract no 2343 with population 775651.4693363408\n",
      "loop no 11.0 for tract no 2343 with population 781721.5555837919\n",
      "loop no 9.0 for tract no 2344 with population 791354.8631754264\n",
      "loop no 9.0 for tract no 2345 with population 764934.261411922\n",
      "loop no 10.0 for tract no 2345 with population 781701.8631514788\n",
      "I am working on tract number 2360 of 3168 tracts\n",
      "loop no 9.0 for tract no 2368 with population 242372.19465634273\n",
      "loop no 10.0 for tract no 2368 with population 557857.7362128386\n",
      "loop no 11.0 for tract no 2368 with population 729819.3602895321\n",
      "loop no 12.0 for tract no 2368 with population 780210.6108728629\n",
      "loop no 9.0 for tract no 2369 with population 319301.5130568893\n",
      "loop no 10.0 for tract no 2369 with population 511236.2019896518\n",
      "loop no 11.0 for tract no 2369 with population 719007.3445858269\n",
      "loop no 12.0 for tract no 2369 with population 757563.394918843\n",
      "loop no 13.0 for tract no 2369 with population 771202.3531133404\n",
      "loop no 14.0 for tract no 2369 with population 779413.2532305517\n",
      "loop no 9.0 for tract no 2373 with population 757150.1129863735\n",
      "loop no 10.0 for tract no 2373 with population 779388.8164126034\n",
      "loop no 9.0 for tract no 2375 with population 803827.6721181587\n",
      "loop no 10.0 for tract no 2375 with population 788536.4837348115\n",
      "I am working on tract number 2380 of 3168 tracts\n",
      "loop no 9.0 for tract no 2381 with population 788399.7184167188\n",
      "loop no 9.0 for tract no 2382 with population 793148.8730845714\n",
      "loop no 9.0 for tract no 2383 with population 800062.7282571971\n",
      "loop no 10.0 for tract no 2383 with population 795030.0668818707\n",
      "loop no 11.0 for tract no 2383 with population 787976.2770198456\n",
      "loop no 9.0 for tract no 2384 with population 787292.2742840514\n",
      "tract,wide, thin angles are  2385 109.0972 70.9028\n",
      "loop no 9.0 for tract no 2386 with population 789287.7992312016\n",
      "loop no 9.0 for tract no 2387 with population 751796.7225537598\n",
      "loop no 10.0 for tract no 2387 with population 773086.2791202199\n",
      "loop no 11.0 for tract no 2387 with population 782862.926267256\n",
      "loop no 9.0 for tract no 2391 with population 778338.920089179\n",
      "loop no 10.0 for tract no 2391 with population 784082.7436606701\n",
      "tract,wide, thin angles are  2394 114.4652 65.5348\n",
      "loop no 9.0 for tract no 2397 with population 767014.1864961744\n",
      "loop no 10.0 for tract no 2397 with population 777628.7226621911\n",
      "loop no 11.0 for tract no 2397 with population 782879.9978293083\n",
      "I am working on tract number 2400 of 3168 tracts\n",
      "I am working on tract number 2420 of 3168 tracts\n",
      "loop no 9.0 for tract no 2431 with population 786290.7800286845\n",
      "loop no 9.0 for tract no 2433 with population 778217.6514832636\n",
      "loop no 10.0 for tract no 2433 with population 783678.1754149063\n",
      "I am working on tract number 2440 of 3168 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2450\n",
      "loop no 9.0 for tract no 2451 with population 993683.7773968714\n",
      "loop no 10.0 for tract no 2451 with population 735598.1595570921\n",
      "loop no 11.0 for tract no 2451 with population 757488.5392228243\n",
      "loop no 12.0 for tract no 2451 with population 777199.6510688723\n",
      "loop no 13.0 for tract no 2451 with population 782536.6224066293\n",
      "loop no 9.0 for tract no 2458 with population 741542.7039824051\n",
      "loop no 10.0 for tract no 2458 with population 774867.3549568244\n",
      "loop no 11.0 for tract no 2458 with population 781691.0730770215\n",
      "I am working on tract number 2460 of 3168 tracts\n",
      "loop no 9.0 for tract no 2462 with population 776772.9127560323\n",
      "loop no 10.0 for tract no 2462 with population 784639.9714947231\n",
      "loop no 9.0 for tract no 2464 with population 777514.0180515451\n",
      "loop no 10.0 for tract no 2464 with population 777564.6754812698\n",
      "loop no 11.0 for tract no 2464 with population 783443.3651720561\n",
      "tract,wide, thin angles are  2466 132.5496 47.4504\n",
      "loop no 9.0 for tract no 2466 with population 684293.7643316142\n",
      "loop no 10.0 for tract no 2466 with population 717996.7688345965\n",
      "loop no 11.0 for tract no 2466 with population 755591.8424245054\n",
      "loop no 12.0 for tract no 2466 with population 774190.6884255083\n",
      "loop no 13.0 for tract no 2466 with population 782047.4822072699\n",
      "loop no 9.0 for tract no 2467 with population 741366.1345842355\n",
      "loop no 10.0 for tract no 2467 with population 770580.4468305529\n",
      "loop no 11.0 for tract no 2467 with population 796743.2429233391\n",
      "loop no 12.0 for tract no 2467 with population 789036.5406993439\n",
      "loop no 9.0 for tract no 2468 with population 776801.0685572874\n",
      "loop no 10.0 for tract no 2468 with population 798690.1170266222\n",
      "loop no 11.0 for tract no 2468 with population 794107.6177951195\n",
      "loop no 9.0 for tract no 2469 with population 774788.6226829921\n",
      "loop no 10.0 for tract no 2469 with population 799031.5944623868\n",
      "loop no 11.0 for tract no 2469 with population 795372.6037743888\n",
      "loop no 12.0 for tract no 2469 with population 778363.8106193878\n",
      "loop no 13.0 for tract no 2469 with population 780604.9622974959\n",
      "loop no 9.0 for tract no 2470 with population 768819.7755988063\n",
      "loop no 10.0 for tract no 2470 with population 779342.9247344024\n",
      "loop no 9.0 for tract no 2472 with population 752784.1532109848\n",
      "loop no 10.0 for tract no 2472 with population 766948.6256233361\n",
      "loop no 11.0 for tract no 2472 with population 780576.6492424577\n",
      "loop no 9.0 for tract no 2473 with population 760303.2832057755\n",
      "loop no 10.0 for tract no 2473 with population 776856.8380663497\n",
      "loop no 11.0 for tract no 2473 with population 787173.7240849051\n",
      "loop no 9.0 for tract no 2474 with population 729435.3506525783\n",
      "loop no 10.0 for tract no 2474 with population 759006.5896053531\n",
      "loop no 11.0 for tract no 2474 with population 775818.1856080543\n",
      "loop no 12.0 for tract no 2474 with population 783059.0579732327\n",
      "tract,wide, thin angles are  2475 88.9403 91.0597\n",
      "loop no 9.0 for tract no 2477 with population 749496.9971075336\n",
      "loop no 10.0 for tract no 2477 with population 779021.517371268\n",
      "loop no 9.0 for tract no 2478 with population 781824.2244817363\n",
      "loop no 9.0 for tract no 2479 with population 757082.5178557386\n",
      "loop no 10.0 for tract no 2479 with population 780545.6092379175\n",
      "I am working on tract number 2480 of 3168 tracts\n",
      "loop no 9.0 for tract no 2480 with population 774607.571161503\n",
      "loop no 10.0 for tract no 2480 with population 784951.5360143986\n",
      "loop no 9.0 for tract no 2481 with population 782409.4336841382\n",
      "loop no 9.0 for tract no 2483 with population 791668.4236177834\n",
      "tract,wide, thin angles are  2484 141.9746 38.0254\n",
      "loop no 9.0 for tract no 2484 with population 773729.6532287722\n",
      "loop no 10.0 for tract no 2484 with population 782437.6137052901\n",
      "loop no 9.0 for tract no 2487 with population 773204.561656371\n",
      "loop no 10.0 for tract no 2487 with population 782114.9370790074\n",
      "loop no 9.0 for tract no 2488 with population 806071.8156492251\n",
      "loop no 10.0 for tract no 2488 with population 784668.8364299404\n",
      "loop no 9.0 for tract no 2489 with population 691898.077274903\n",
      "loop no 10.0 for tract no 2489 with population 741565.4121489314\n",
      "loop no 11.0 for tract no 2489 with population 785848.0504520414\n",
      "loop no 9.0 for tract no 2490 with population 683575.177586738\n",
      "loop no 10.0 for tract no 2490 with population 724389.1421287621\n",
      "loop no 11.0 for tract no 2490 with population 762468.3242540266\n",
      "loop no 12.0 for tract no 2490 with population 778504.7058614572\n",
      "loop no 13.0 for tract no 2490 with population 782015.3750260605\n",
      "loop no 9.0 for tract no 2491 with population 737240.7093156732\n",
      "loop no 10.0 for tract no 2491 with population 766754.2040175117\n",
      "loop no 11.0 for tract no 2491 with population 767939.9754956204\n",
      "loop no 12.0 for tract no 2491 with population 777877.0588652277\n",
      "loop no 13.0 for tract no 2491 with population 782462.755972186\n",
      "loop no 9.0 for tract no 2492 with population 768341.975701187\n",
      "loop no 10.0 for tract no 2492 with population 776596.94595216\n",
      "loop no 11.0 for tract no 2492 with population 783557.9779384087\n",
      "tract,wide, thin angles are  2493 127.9554 52.0446\n",
      "loop no 9.0 for tract no 2493 with population 773965.8455812678\n",
      "loop no 10.0 for tract no 2493 with population 782716.8602152112\n",
      "loop no 9.0 for tract no 2494 with population 759829.9625538588\n",
      "loop no 10.0 for tract no 2494 with population 775928.9356354643\n",
      "loop no 11.0 for tract no 2494 with population 784423.7780153075\n",
      "loop no 9.0 for tract no 2495 with population 772553.397573094\n",
      "loop no 10.0 for tract no 2495 with population 782266.147311233\n",
      "loop no 9.0 for tract no 2496 with population 739519.3146257941\n",
      "loop no 10.0 for tract no 2496 with population 772269.0337944023\n",
      "loop no 11.0 for tract no 2496 with population 781437.4859929708\n",
      "loop no 9.0 for tract no 2499 with population 791487.2086830235\n",
      "I am working on tract number 2500 of 3168 tracts\n",
      "loop no 9.0 for tract no 2500 with population 782741.052784522\n",
      "loop no 9.0 for tract no 2501 with population 691595.5271124254\n",
      "loop no 10.0 for tract no 2501 with population 771705.2361394619\n",
      "loop no 11.0 for tract no 2501 with population 785978.8441003582\n",
      "loop no 9.0 for tract no 2509 with population 777723.6299295642\n",
      "loop no 10.0 for tract no 2509 with population 787910.3720443656\n",
      "loop no 9.0 for tract no 2510 with population 750970.495632815\n",
      "loop no 10.0 for tract no 2510 with population 775353.8873330252\n",
      "loop no 11.0 for tract no 2510 with population 784275.0917366944\n",
      "tract,wide, thin angles are  2511 131.4585 48.5415\n",
      "loop no 9.0 for tract no 2511 with population 700836.2404632757\n",
      "loop no 10.0 for tract no 2511 with population 734362.7533337631\n",
      "loop no 11.0 for tract no 2511 with population 767245.9786929467\n",
      "loop no 12.0 for tract no 2511 with population 779569.2881951653\n",
      "I am working on tract number 2520 of 3168 tracts\n",
      "tract,wide, thin angles are  2529 142.5026 37.4974\n",
      "loop no 9.0 for tract no 2532 with population 683888.0077516681\n",
      "loop no 10.0 for tract no 2532 with population 685932.5415225078\n",
      "loop no 11.0 for tract no 2532 with population 728591.0330783965\n",
      "loop no 12.0 for tract no 2532 with population 758513.9217401418\n",
      "loop no 13.0 for tract no 2532 with population 770289.6323463825\n",
      "loop no 14.0 for tract no 2532 with population 773498.9368278433\n",
      "loop no 15.0 for tract no 2532 with population 779285.4856117346\n",
      "I am working on tract number 2540 of 3168 tracts\n",
      "tract,wide, thin angles are  2547 131.1422 48.8578\n",
      "loop no 9.0 for tract no 2558 with population 754733.5460948271\n",
      "loop no 10.0 for tract no 2558 with population 780488.4271234376\n",
      "I am working on tract number 2560 of 3168 tracts\n",
      "loop no 9.0 for tract no 2561 with population 770602.0565926435\n",
      "loop no 10.0 for tract no 2561 with population 772579.469826207\n",
      "loop no 11.0 for tract no 2561 with population 774287.8374626671\n",
      "loop no 12.0 for tract no 2561 with population 774323.2701203807\n",
      "loop no 13.0 for tract no 2561 with population 780658.5578754246\n",
      "loop no 9.0 for tract no 2563 with population 659840.0846990817\n",
      "loop no 10.0 for tract no 2563 with population 732829.8759819887\n",
      "loop no 11.0 for tract no 2563 with population 763533.7754229306\n",
      "loop no 12.0 for tract no 2563 with population 774805.9806346043\n",
      "loop no 13.0 for tract no 2563 with population 780566.3885395832\n",
      "tract,wide, thin angles are  2565 129.7186 50.2814\n",
      "loop no 9.0 for tract no 2565 with population 699206.0055885116\n",
      "loop no 10.0 for tract no 2565 with population 738338.8785669426\n",
      "loop no 11.0 for tract no 2565 with population 765568.1374611207\n",
      "loop no 12.0 for tract no 2565 with population 777599.9585223468\n",
      "loop no 13.0 for tract no 2565 with population 782391.3856802202\n",
      "we have 2 non-opposing shorted wedges for tract no 2572\n",
      "loop no 9.0 for tract no 2579 with population 767860.5650143703\n",
      "loop no 10.0 for tract no 2579 with population 828875.2557375508\n",
      "loop no 11.0 for tract no 2579 with population 802019.3503400258\n",
      "loop no 12.0 for tract no 2579 with population 791207.6781681189\n",
      "I am working on tract number 2580 of 3168 tracts\n",
      "loop no 9.0 for tract no 2580 with population 784890.1006795799\n",
      "loop no 9.0 for tract no 2581 with population 783496.9857491747\n",
      "tract,wide, thin angles are  2583 143.415 36.585\n",
      "loop no 9.0 for tract no 2586 with population 780775.9331599319\n",
      "loop no 9.0 for tract no 2590 with population 784731.1856166751\n",
      "tract,wide, thin angles are  2592 129.388 50.612\n",
      "loop no 9.0 for tract no 2599 with population 768891.3929617353\n",
      "loop no 10.0 for tract no 2599 with population 781797.787838666\n",
      "I am working on tract number 2600 of 3168 tracts\n",
      "loop no 9.0 for tract no 2602 with population 784125.0692685838\n",
      "we have 2 non-opposing shorted wedges for tract no 2603\n",
      "we have 2 non-opposing shorted wedges for tract no 2604\n",
      "we have 2 non-opposing shorted wedges for tract no 2605\n",
      "we have 2 non-opposing shorted wedges for tract no 2606\n",
      "loop no 9.0 for tract no 2607 with population 784735.0864579591\n",
      "loop no 9.0 for tract no 2609 with population 781637.4957113635\n",
      "loop no 9.0 for tract no 2613 with population 772158.352417572\n",
      "loop no 10.0 for tract no 2613 with population 781850.4083744204\n",
      "loop no 9.0 for tract no 2614 with population 675342.5890466615\n",
      "loop no 10.0 for tract no 2614 with population 749947.9367733177\n",
      "loop no 11.0 for tract no 2614 with population 766347.1751326384\n",
      "loop no 12.0 for tract no 2614 with population 774445.6234402587\n",
      "loop no 13.0 for tract no 2614 with population 781055.0930980701\n",
      "loop no 9.0 for tract no 2617 with population 812181.0319379712\n",
      "loop no 10.0 for tract no 2617 with population 787802.8805226777\n",
      "I am working on tract number 2620 of 3168 tracts\n",
      "loop no 9.0 for tract no 2633 with population 783368.8717839768\n",
      "I am working on tract number 2640 of 3168 tracts\n",
      "loop no 9.0 for tract no 2644 with population 221688.42655077847\n",
      "loop no 10.0 for tract no 2644 with population 544179.2875645924\n",
      "loop no 11.0 for tract no 2644 with population 776506.5015234818\n",
      "loop no 12.0 for tract no 2644 with population 753163.0389343791\n",
      "loop no 13.0 for tract no 2644 with population 769461.2897203012\n",
      "loop no 14.0 for tract no 2644 with population 777737.2692875555\n",
      "loop no 15.0 for tract no 2644 with population 781547.9068702\n",
      "loop no 9.0 for tract no 2645 with population 790235.043420055\n",
      "tract,wide, thin angles are  2646 118.922 61.078\n",
      "loop no 9.0 for tract no 2646 with population 802751.252650118\n",
      "loop no 10.0 for tract no 2646 with population 785368.7703582637\n",
      "loop no 9.0 for tract no 2647 with population 823911.8198966975\n",
      "loop no 10.0 for tract no 2647 with population 791817.5392973056\n",
      "loop no 9.0 for tract no 2649 with population 788594.6662797305\n",
      "loop no 9.0 for tract no 2650 with population 807754.8235145452\n",
      "loop no 10.0 for tract no 2650 with population 795774.968869552\n",
      "loop no 11.0 for tract no 2650 with population 788446.866007558\n",
      "loop no 9.0 for tract no 2653 with population 771773.0352375058\n",
      "loop no 10.0 for tract no 2653 with population 779098.0960203762\n",
      "we have 2 non-opposing shorted wedges for tract no 2654\n",
      "we have 2 non-opposing shorted wedges for tract no 2655\n",
      "we have 2 non-opposing shorted wedges for tract no 2656\n",
      "we have 2 non-opposing shorted wedges for tract no 2657\n",
      "we have 2 non-opposing shorted wedges for tract no 2659\n",
      "I am working on tract number 2660 of 3168 tracts\n",
      "loop no 9.0 for tract no 2661 with population 790260.5251932582\n",
      "loop no 9.0 for tract no 2662 with population 782588.2578907728\n",
      "we have 2 non-opposing shorted wedges for tract no 2663\n",
      "tract,wide, thin angles are  2664 138.2583 41.7417\n",
      "loop no 9.0 for tract no 2664 with population 895423.4599265114\n",
      "loop no 10.0 for tract no 2664 with population 825666.7439965198\n",
      "loop no 11.0 for tract no 2664 with population 791544.6591752185\n",
      "loop no 9.0 for tract no 2665 with population 961225.6262346193\n",
      "loop no 10.0 for tract no 2665 with population 885650.2918231284\n",
      "loop no 11.0 for tract no 2665 with population 830846.7793782522\n",
      "loop no 12.0 for tract no 2665 with population 794995.7024115713\n",
      "loop no 13.0 for tract no 2665 with population 790351.3709060557\n",
      "loop no 9.0 for tract no 2666 with population 784835.692399085\n",
      "loop no 9.0 for tract no 2667 with population 774145.1622326829\n",
      "loop no 10.0 for tract no 2667 with population 769018.9727337062\n",
      "loop no 11.0 for tract no 2667 with population 778916.5946724645\n",
      "loop no 9.0 for tract no 2669 with population 782303.7628337088\n",
      "loop no 9.0 for tract no 2670 with population 711987.0798585754\n",
      "loop no 10.0 for tract no 2670 with population 774905.7164776403\n",
      "loop no 11.0 for tract no 2670 with population 783730.8837970297\n",
      "loop no 9.0 for tract no 2671 with population 745381.2217657822\n",
      "loop no 10.0 for tract no 2671 with population 778026.1944198383\n",
      "loop no 11.0 for tract no 2671 with population 784618.6974426052\n",
      "loop no 9.0 for tract no 2674 with population 782726.1373546834\n",
      "I am working on tract number 2680 of 3168 tracts\n",
      "loop no 9.0 for tract no 2687 with population 760514.8502311182\n",
      "loop no 10.0 for tract no 2687 with population 770522.7894466952\n",
      "loop no 11.0 for tract no 2687 with population 781594.7846870353\n",
      "loop no 9.0 for tract no 2690 with population 559571.4490557103\n",
      "loop no 10.0 for tract no 2690 with population 675920.9789749486\n",
      "loop no 11.0 for tract no 2690 with population 707767.0674532171\n",
      "loop no 12.0 for tract no 2690 with population 773768.3584814547\n",
      "loop no 13.0 for tract no 2690 with population 777235.0153819484\n",
      "loop no 14.0 for tract no 2690 with population 782721.3812371462\n",
      "tract,wide, thin angles are  2691 101.6412 78.3588\n",
      "loop no 9.0 for tract no 2691 with population 780245.8960211829\n",
      "we have 2 non-opposing shorted wedges for tract no 2693\n",
      "I am working on tract number 2700 of 3168 tracts\n",
      "loop no 9.0 for tract no 2708 with population 777246.2233870127\n",
      "loop no 10.0 for tract no 2708 with population 783344.9628708485\n",
      "tract,wide, thin angles are  2709 95.9213 84.0787\n",
      "loop no 9.0 for tract no 2709 with population 771683.8751045524\n",
      "loop no 10.0 for tract no 2709 with population 780735.4553215449\n",
      "loop no 9.0 for tract no 2714 with population 775216.9273022279\n",
      "loop no 10.0 for tract no 2714 with population 782984.9492940777\n",
      "loop no 9.0 for tract no 2719 with population 784961.5083808269\n",
      "I am working on tract number 2720 of 3168 tracts\n",
      "tract,wide, thin angles are  2727 143.1828 36.8172\n",
      "loop no 9.0 for tract no 2735 with population 762600.7879658132\n",
      "loop no 10.0 for tract no 2735 with population 774691.5848690482\n",
      "loop no 11.0 for tract no 2735 with population 780659.8718311555\n",
      "tract,wide, thin angles are  2736 128.2704 51.7296\n",
      "loop no 9.0 for tract no 2737 with population 725306.96159081\n",
      "loop no 10.0 for tract no 2737 with population 837644.8933754164\n",
      "loop no 11.0 for tract no 2737 with population 775710.4581284181\n",
      "loop no 12.0 for tract no 2737 with population 780249.1912745219\n",
      "loop no 9.0 for tract no 2738 with population 778507.9231144171\n",
      "loop no 10.0 for tract no 2738 with population 787696.0938147779\n",
      "I am working on tract number 2740 of 3168 tracts\n",
      "loop no 9.0 for tract no 2743 with population 786574.3884964634\n",
      "loop no 9.0 for tract no 2751 with population 771142.2447299254\n",
      "loop no 10.0 for tract no 2751 with population 783504.847184256\n",
      "loop no 9.0 for tract no 2752 with population 783188.7488722303\n",
      "loop no 9.0 for tract no 2753 with population 784851.3724936116\n",
      "tract,wide, thin angles are  2754 100.9758 79.0242\n",
      "loop no 9.0 for tract no 2754 with population 638491.7579473329\n",
      "loop no 10.0 for tract no 2754 with population 752884.4448808058\n",
      "loop no 11.0 for tract no 2754 with population 780310.8089377193\n",
      "loop no 9.0 for tract no 2756 with population 833030.5725749711\n",
      "loop no 10.0 for tract no 2756 with population 770819.2325974714\n",
      "loop no 11.0 for tract no 2756 with population 779585.7509197311\n",
      "loop no 9.0 for tract no 2757 with population 663349.503523357\n",
      "loop no 10.0 for tract no 2757 with population 775035.691674442\n",
      "loop no 11.0 for tract no 2757 with population 782462.0575822715\n",
      "loop no 9.0 for tract no 2758 with population 763434.9564814699\n",
      "loop no 10.0 for tract no 2758 with population 779189.9585491621\n",
      "I am working on tract number 2760 of 3168 tracts\n",
      "loop no 9.0 for tract no 2766 with population 286688.63860048377\n",
      "loop no 10.0 for tract no 2766 with population 620468.904688754\n",
      "loop no 11.0 for tract no 2766 with population 756534.902617693\n",
      "loop no 12.0 for tract no 2766 with population 761036.8594758679\n",
      "loop no 13.0 for tract no 2766 with population 777348.129355541\n",
      "loop no 14.0 for tract no 2766 with population 782085.6311520042\n",
      "loop no 9.0 for tract no 2770 with population 765550.7905308818\n",
      "loop no 10.0 for tract no 2770 with population 782991.1467698636\n",
      "tract,wide, thin angles are  2772 105.5459 74.4541\n",
      "loop no 9.0 for tract no 2772 with population 761744.9066573894\n",
      "loop no 10.0 for tract no 2772 with population 789353.4975963435\n",
      "I am working on tract number 2780 of 3168 tracts\n",
      "loop no 9.0 for tract no 2782 with population 782584.7548000354\n",
      "loop no 9.0 for tract no 2783 with population 751625.5065149395\n",
      "loop no 10.0 for tract no 2783 with population 789228.8383799795\n",
      "loop no 9.0 for tract no 2789 with population 787504.6938720626\n",
      "we have 2 non-opposing shorted wedges for tract no 2790\n",
      "loop no 9.0 for tract no 2796 with population 780961.0372904511\n",
      "I am working on tract number 2800 of 3168 tracts\n",
      "loop no 9.0 for tract no 2801 with population 782762.349342662\n",
      "loop no 9.0 for tract no 2802 with population 751243.6004387526\n",
      "loop no 10.0 for tract no 2802 with population 776206.9078685677\n",
      "loop no 11.0 for tract no 2802 with population 783361.2017186511\n",
      "loop no 9.0 for tract no 2806 with population 480657.44987727434\n",
      "loop no 10.0 for tract no 2806 with population 647855.935350372\n",
      "loop no 11.0 for tract no 2806 with population 699226.6925512371\n",
      "loop no 12.0 for tract no 2806 with population 701695.33047088\n",
      "loop no 13.0 for tract no 2806 with population 741083.2173386872\n",
      "loop no 14.0 for tract no 2806 with population 757442.9159432494\n",
      "loop no 15.0 for tract no 2806 with population 768026.5160532986\n",
      "loop no 16.0 for tract no 2806 with population 774113.4459472871\n",
      "loop no 17.0 for tract no 2806 with population 778988.5732196297\n",
      "tract,wide, thin angles are  2808 142.2679 37.7321\n",
      "loop no 9.0 for tract no 2808 with population 747066.3776594783\n",
      "loop no 10.0 for tract no 2808 with population 764055.1198844727\n",
      "loop no 11.0 for tract no 2808 with population 780378.3397084489\n",
      "loop no 9.0 for tract no 2813 with population 784579.8580409985\n",
      "loop no 9.0 for tract no 2814 with population 750461.2404943076\n",
      "loop no 10.0 for tract no 2814 with population 765393.3878178273\n",
      "loop no 11.0 for tract no 2814 with population 781789.9939430428\n",
      "loop no 9.0 for tract no 2815 with population 774138.2913717602\n",
      "loop no 10.0 for tract no 2815 with population 782135.7564735683\n",
      "loop no 9.0 for tract no 2816 with population 798786.5942702877\n",
      "loop no 10.0 for tract no 2816 with population 779937.183787724\n",
      "tract,wide, thin angles are  2817 109.7501 70.2499\n",
      "loop no 9.0 for tract no 2819 with population 784853.0318080146\n",
      "I am working on tract number 2820 of 3168 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2824\n",
      "we have 2 non-opposing shorted wedges for tract no 2825\n",
      "tract,wide, thin angles are  2826 143.9782 36.0218\n",
      "loop no 9.0 for tract no 2826 with population 781835.8034737153\n",
      "loop no 9.0 for tract no 2828 with population 762525.9974583371\n",
      "loop no 10.0 for tract no 2828 with population 781495.8723875368\n",
      "loop no 9.0 for tract no 2829 with population 776808.5179822356\n",
      "loop no 10.0 for tract no 2829 with population 783999.5510671387\n",
      "loop no 9.0 for tract no 2830 with population 790672.2085648777\n",
      "tract,wide, thin angles are  2835 143.6894 36.3106\n",
      "loop no 9.0 for tract no 2835 with population 748148.2672771688\n",
      "loop no 10.0 for tract no 2835 with population 758566.4041113192\n",
      "loop no 11.0 for tract no 2835 with population 778459.6587411242\n",
      "loop no 12.0 for tract no 2835 with population 783393.8384789673\n",
      "I am working on tract number 2840 of 3168 tracts\n",
      "loop no 9.0 for tract no 2840 with population 777515.3658381723\n",
      "loop no 10.0 for tract no 2840 with population 783522.891951942\n",
      "loop no 9.0 for tract no 2841 with population 747464.1322797037\n",
      "loop no 10.0 for tract no 2841 with population 773712.6190143386\n",
      "loop no 11.0 for tract no 2841 with population 781804.874761632\n",
      "loop no 9.0 for tract no 2843 with population 755957.2410229619\n",
      "loop no 10.0 for tract no 2843 with population 758674.956297934\n",
      "loop no 11.0 for tract no 2843 with population 777430.4970474321\n",
      "loop no 12.0 for tract no 2843 with population 782877.6241722816\n",
      "tract,wide, thin angles are  2844 141.567 38.433\n",
      "loop no 9.0 for tract no 2844 with population 780021.1567191414\n",
      "we have 2 non-opposing shorted wedges for tract no 2846\n",
      "tract,wide, thin angles are  2853 133.7879 46.2121\n",
      "loop no 9.0 for tract no 2858 with population 780236.5415571041\n",
      "I am working on tract number 2860 of 3168 tracts\n",
      "loop no 9.0 for tract no 2869 with population 785460.1992176817\n",
      "loop no 9.0 for tract no 2870 with population 791717.4767792423\n",
      "loop no 9.0 for tract no 2877 with population 751580.866470896\n",
      "loop no 10.0 for tract no 2877 with population 777380.1298910809\n",
      "loop no 11.0 for tract no 2877 with population 783945.7723184103\n",
      "I am working on tract number 2880 of 3168 tracts\n",
      "loop no 9.0 for tract no 2882 with population 779006.0630575136\n",
      "loop no 9.0 for tract no 2888 with population 753440.6989424033\n",
      "loop no 10.0 for tract no 2888 with population 797434.51219815\n",
      "loop no 11.0 for tract no 2888 with population 793116.822034162\n",
      "tract,wide, thin angles are  2889 142.9681 37.0319\n",
      "loop no 9.0 for tract no 2889 with population 745535.0528185413\n",
      "loop no 10.0 for tract no 2889 with population 773349.9959974821\n",
      "loop no 11.0 for tract no 2889 with population 781346.7730867914\n",
      "loop no 9.0 for tract no 2896 with population 793262.6422319352\n",
      "I am working on tract number 2900 of 3168 tracts\n",
      "loop no 9.0 for tract no 2901 with population 792026.586295744\n",
      "loop no 9.0 for tract no 2906 with population 789373.5175900254\n",
      "tract,wide, thin angles are  2907 124.3349 55.6651\n",
      "loop no 9.0 for tract no 2908 with population 780285.886006221\n",
      "loop no 9.0 for tract no 2911 with population 763714.6520017115\n",
      "loop no 10.0 for tract no 2911 with population 779970.359354912\n",
      "loop no 9.0 for tract no 2914 with population 787635.893945954\n",
      "loop no 9.0 for tract no 2915 with population 805332.496825354\n",
      "loop no 10.0 for tract no 2915 with population 805332.0341271351\n",
      "loop no 11.0 for tract no 2915 with population 805331.9175800618\n",
      "loop no 12.0 for tract no 2915 with population 805331.8806604012\n",
      "loop no 13.0 for tract no 2915 with population 805331.8668112664\n",
      "loop no 14.0 for tract no 2915 with population 805331.8608893135\n",
      "loop no 15.0 for tract no 2915 with population 805331.8580773037\n",
      "loop no 16.0 for tract no 2915 with population 805331.8565146762\n",
      "loop no 17.0 for tract no 2915 with population 805331.8553568929\n",
      "loop no 18.0 for tract no 2915 with population 805331.8545056162\n",
      "loop no 19.0 for tract no 2915 with population 8.725045315548249\n",
      "loop no 20.0 for tract no 2915 with population 345827.0829659953\n",
      "loop no 21.0 for tract no 2915 with population 490393.8782714947\n",
      "loop no 22.0 for tract no 2915 with population 613833.8916960268\n",
      "loop no 23.0 for tract no 2915 with population 749457.5856216687\n",
      "loop no 24.0 for tract no 2915 with population 760540.8108841559\n",
      "loop no 25.0 for tract no 2915 with population 768807.5785413744\n",
      "loop no 26.0 for tract no 2915 with population 777070.082644267\n",
      "loop no 27.0 for tract no 2915 with population 781168.6365407903\n",
      "tract,wide, thin angles are  2916 120.1564 59.8436\n",
      "I am working on tract number 2920 of 3168 tracts\n",
      "loop no 9.0 for tract no 2924 with population 781433.9779084156\n",
      "tract,wide, thin angles are  2925 134.8708 45.1292\n",
      "loop no 9.0 for tract no 2938 with population 783498.9604008348\n",
      "I am working on tract number 2940 of 3168 tracts\n",
      "tract,wide, thin angles are  2943 133.5992 46.4008\n",
      "loop no 9.0 for tract no 2946 with population 792471.636655421\n",
      "loop no 9.0 for tract no 2951 with population 708076.9577305786\n",
      "loop no 10.0 for tract no 2951 with population 742513.9032852203\n",
      "loop no 11.0 for tract no 2951 with population 764867.8464047243\n",
      "loop no 12.0 for tract no 2951 with population 790744.0183705487\n",
      "loop no 9.0 for tract no 2954 with population 785474.9397597462\n",
      "loop no 9.0 for tract no 2955 with population 787290.766378024\n",
      "loop no 9.0 for tract no 2956 with population 770267.5923976706\n",
      "loop no 10.0 for tract no 2956 with population 779061.9205826462\n",
      "we have 2 non-opposing shorted wedges for tract no 2959\n",
      "I am working on tract number 2960 of 3168 tracts\n",
      "tract,wide, thin angles are  2961 131.9448 48.0552\n",
      "we have 2 non-opposing shorted wedges for tract no 2969\n",
      "tract,wide, thin angles are  2970 98.8391 81.1609\n",
      "loop no 9.0 for tract no 2970 with population 775772.4448411288\n",
      "loop no 10.0 for tract no 2970 with population 778226.3522726754\n",
      "loop no 11.0 for tract no 2970 with population 791077.6212901662\n",
      "loop no 9.0 for tract no 2973 with population 765124.4606809202\n",
      "loop no 10.0 for tract no 2973 with population 779042.1416016894\n",
      "loop no 9.0 for tract no 2974 with population 790932.097190975\n",
      "tract,wide, thin angles are  2979 129.86 50.14\n",
      "loop no 9.0 for tract no 2979 with population 782533.2514943491\n",
      "I am working on tract number 2980 of 3168 tracts\n",
      "loop no 9.0 for tract no 2981 with population 783855.9496550402\n",
      "loop no 9.0 for tract no 2982 with population 684541.3884554444\n",
      "loop no 10.0 for tract no 2982 with population 738920.8381484994\n",
      "loop no 11.0 for tract no 2982 with population 769469.6565462002\n",
      "loop no 12.0 for tract no 2982 with population 779107.9213718184\n",
      "loop no 9.0 for tract no 2983 with population 742344.9570411115\n",
      "loop no 10.0 for tract no 2983 with population 764089.3787089433\n",
      "loop no 11.0 for tract no 2983 with population 778217.3192191471\n",
      "loop no 12.0 for tract no 2983 with population 783323.591733247\n",
      "tract,wide, thin angles are  2988 142.0719 37.9281\n",
      "loop no 9.0 for tract no 2988 with population 788694.773615748\n",
      "loop no 9.0 for tract no 2990 with population 786985.1559130955\n",
      "loop no 9.0 for tract no 2991 with population 717965.3020616481\n",
      "loop no 10.0 for tract no 2991 with population 754981.2697257046\n",
      "loop no 11.0 for tract no 2991 with population 776656.8678876265\n",
      "loop no 12.0 for tract no 2991 with population 781806.6651177763\n",
      "I am working on tract number 3000 of 3168 tracts\n",
      "loop no 9.0 for tract no 3001 with population 767099.5313557788\n",
      "loop no 10.0 for tract no 3001 with population 787863.0870724777\n",
      "loop no 9.0 for tract no 3013 with population 772739.6108461493\n",
      "loop no 10.0 for tract no 3013 with population 783891.5338301506\n",
      "I am working on tract number 3020 of 3168 tracts\n",
      "loop no 9.0 for tract no 3031 with population 739438.3210919644\n",
      "loop no 10.0 for tract no 3031 with population 772948.6654932568\n",
      "loop no 11.0 for tract no 3031 with population 782230.6828110495\n",
      "I am working on tract number 3040 of 3168 tracts\n",
      "loop no 9.0 for tract no 3052 with population 780201.4681207038\n",
      "loop no 9.0 for tract no 3054 with population 777889.9915437449\n",
      "loop no 10.0 for tract no 3054 with population 783987.6128805715\n",
      "I am working on tract number 3060 of 3168 tracts\n",
      "loop no 9.0 for tract no 3072 with population 765331.178013928\n",
      "loop no 10.0 for tract no 3072 with population 784505.9883420353\n",
      "loop no 9.0 for tract no 3073 with population 763036.7246440692\n",
      "loop no 10.0 for tract no 3073 with population 779297.623627183\n",
      "loop no 9.0 for tract no 3074 with population 769129.6828348285\n",
      "loop no 10.0 for tract no 3074 with population 784565.3798112313\n",
      "loop no 9.0 for tract no 3075 with population 786904.7174829104\n",
      "we have 2 non-opposing shorted wedges for tract no 3076\n",
      "I am working on tract number 3080 of 3168 tracts\n",
      "I am working on tract number 3100 of 3168 tracts\n",
      "loop no 9.0 for tract no 3107 with population 784938.1583646291\n",
      "loop no 9.0 for tract no 3109 with population 783019.5050468312\n",
      "loop no 9.0 for tract no 3112 with population 718388.5963524729\n",
      "loop no 10.0 for tract no 3112 with population 781422.6715144345\n",
      "loop no 9.0 for tract no 3113 with population 766716.2213113299\n",
      "loop no 10.0 for tract no 3113 with population 779946.539684091\n",
      "tract,wide, thin angles are  3114 113.5861 66.4139\n",
      "loop no 9.0 for tract no 3115 with population 784187.3944463149\n",
      "loop no 9.0 for tract no 3116 with population 797371.313406446\n",
      "loop no 10.0 for tract no 3116 with population 790598.1783813522\n",
      "loop no 9.0 for tract no 3117 with population 785694.372886403\n",
      "loop no 9.0 for tract no 3119 with population 762878.8335366116\n",
      "loop no 10.0 for tract no 3119 with population 766376.5225731796\n",
      "loop no 11.0 for tract no 3119 with population 779433.4766206874\n",
      "I am working on tract number 3120 of 3168 tracts\n",
      "loop no 9.0 for tract no 3120 with population 778819.2877563993\n",
      "loop no 9.0 for tract no 3121 with population 777831.8800674509\n",
      "loop no 10.0 for tract no 3121 with population 786076.5463987361\n",
      "tract,wide, thin angles are  3123 109.2822 70.7178\n",
      "loop no 9.0 for tract no 3123 with population 786080.1693705625\n",
      "I am working on tract number 3140 of 3168 tracts\n",
      "I am working on tract number 3160 of 3168 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000007372524545\n",
      "Average and max number of wedgePop loops per tract were:  6.589015151515151 30.0\n",
      "min,average,max of Home District areas were:  0.0 0.7320280846051762 2.7538489372295043\n",
      "calculated statewide vote was 0.5421053897511401, should have been 0.5407669764770272\n",
      "calcd statewide Hispanic pop was 0.04371884099746297, should have been 0.04418071082647256\n",
      "calcd statewide Black pop was 0.12516928817574072, should have been 0.1253262864500102\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.411300505050505\n"
     ]
    }
   ],
   "source": [
    "#1/30/22 no longer using wedge density in wedgePop guesses, Black and Hisp included, from Texas, step-size guardrails\n",
    "# An offshoot of the stateHD code, but responds differently when a wedge falls short of targetWedge Pop\n",
    "minTractPop = 100  #this is usually set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon\n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "guessedR = [0.]*nWedges  #wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in all HD's vs. expectation\n",
    "loopTractUse = [0.] * nTracts  #same, but for the current loop / tract\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #***NOT YET VETTED geographical area of each home district\n",
    "toler = 0.01  #adjustable - fractional slop in district population.  normally 0.01\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 8  #at this loop number, we alert user of problem\n",
    "nLoopFix = 18 #at this loop number, we will shut off a low-pop active wedge\n",
    "nGiveUp = 30\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "globalMax_dr = 0.5*(MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "printAngle = [45.] * nTracts  #for debugging, tracking the exit angle.  Set at 45 if not triggered\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8  #fraction of pi for maxWideAngle\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # estimated radius of densest district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    guessedR = [ (avgDistrictPop / homeGridDensity / pi)**0.5 ]*nWedges  #*** IS THIS EVEN USED?  CORRECT?\n",
    "    randomAngle = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.\n",
    "    sumPartialWedgePops = 0.  #this will track total pop of wedges that ran into a boundary before meeting quota\n",
    "    nActiveWedges = nWedges\n",
    "    targetWedgePop = avgDistrictPop / nWedges  #at beginning, all wedges are active\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgeComplete = [\"No\"]*nWedges\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    wedgeTotGOP = [0.]*nWedges\n",
    "    wedgeHisp = [0.]*nWedges\n",
    "    wedgeBlack = [0.]*nWedges\n",
    "    wedgeTotVote = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+randomAngle   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #min prevents rare div-by-zero\n",
    "        if tractPop[t] > minTractPop : #skip minorities for negligible tracts to avoid div/0\n",
    "            wedgeHisp[nW] = tractHisp[t]/tractPop[t]*wedgePop[nW]\n",
    "            wedgeBlack[nW] = tractBlack[t]/tractPop[t]*wedgePop[nW]\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0): #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            if isOverEdge[nW] != 1:    #...except we skip underpopulated wedges with no room to grow\n",
    "                neededWedgePop = targetWedgePop - wedgePop[nW]  #note that target is not updated until all nW looped\n",
    "\n",
    "                R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]\n",
    "                if (abs(wedgePopDelta) < 0.001 ):  #avoid divide-by-zero in sparse areas\n",
    "                    wedgePopDelta = 0.001  #assume we need to grow if we didn't pick up or lose much pop in last loop\n",
    "                guessedR2 = max(tinyR*tinyR,currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop)\n",
    "                guessed_dr = guessedR2 ** 0.5 - currentR[nW]\n",
    "                tentative_dr = gain * guessed_dr   #gain typically ~0.7 for stability\n",
    "                if (tentative_dr > max_dr[nW]):  #in danger of taking too big of a positive step\n",
    "                    tentative_dr = max_dr[nW]\n",
    "                if tentative_dr < -0.3 * max_dr[nW] :\n",
    "                    tentative_dr = -0.3 * max_dr[nW]    #don't shrink wedge too drastically\n",
    "                if tentative_dr < -0.6 * currentR[nW]  :\n",
    "                    tentative_dr = -0.6 * currentR[nW]\n",
    "                    if old_dr[nW] > 0 :  #we are yo-yoing; this step negative but last step was positive\n",
    "                        max_dr[nW] = 0.5*max_dr[nW]  #reduce future step size                        \n",
    "                \n",
    "                dr = tentative_dr\n",
    "                # old_dr[nW] = dr  #for next time around\n",
    "                WP = round(wedgePop[nW],1)\n",
    "                oWP = round(oldWedgePop[nW],1)\n",
    "                nWP = round(neededWedgePop,3)\n",
    "                curR = round(currentR[nW],2)\n",
    "                g_dr = round(guessed_dr,2)\n",
    "                #if (nW == 0):\n",
    "                #print(targetWedgePop,\" (tWP),     tract, loop, wedgeNo, wedgePop, oWP,nWP, currR, gues_dr, dr,R2delta\")\n",
    "                #print(t,int(tractLoopCounter[t]),nW,WP,oWP,nWP,curR,g_dr, round(dr,4), round(R2delta,3))\n",
    "                # current values are now \"old\" values ...\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                latestWedgeHisp = 0.\n",
    "                latestWedgeBlack = 0.\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    latestWedgeBlack += fracArea*tractBlack[nTT]\n",
    "                                    latestWedgeHisp += fracArea*tractHisp[nTT]\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        # now do precincts\n",
    "                        for pp in range(nGridPrecincts[nG]):  #same comments as above loop, but now for precincts to get R lean\n",
    "                            nPP = gridPrecinctNo[nG][pp]\n",
    "                            if usedPrecinct[nPP] == 0  :\n",
    "                                usedPrecinct[nPP] = 1\n",
    "                                overlap = vtdGeom[nPP].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/vtdArea[nPP]\n",
    "                                    wedgeTotGOP[nW] += np.sign(dr)*fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                                    wedgeTotVote[nW] += np.sign(dr)*fracArea*vtdPop[nPP]\n",
    "                                    loopPrecinctUse[nPP] += np.sign(dr)*overlap/vtdArea[nPP] * tractPop[t]/avgDistrictPop\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                wedgeHisp[nW] +=np.sign(dr)*latestWedgeHisp\n",
    "                wedgeBlack[nW] +=np.sign(dr)*latestWedgeBlack\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop : #if we plan to increase wedge size, stop if we're beyond buffered boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    # alt to below to try: if leadingEdge.centroid.intersects(bufferedMAP)\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #just sayin....\n",
    "                    else:  #our wedge is fully beyond the map.  since map is fairly convex, give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        wedgePopGap[nW] = avgDistrictPop/nWedges - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1\n",
    "                        sumPartialWedgePops += wedgePop[nW]\n",
    "                        if (nActiveWedges == 0) : #we're doomed\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                        \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop\n",
    "        targetWedgePop = (avgDistrictPop - sumPartialWedgePops )/nActiveWedges  #active wedges to pick up any slack\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #we adjust wedge angles at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                shortW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the 2nd shorted wedge\n",
    "                if (isOverEdge[shortW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            shortW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[shortW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    wedgeAngle=[avgWedgeAngle]*nWedges\n",
    "            else: # a single shorted wedge (over boundary).  Completely re-start wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                startAngle = exitAngle #                \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation   # *********************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0.\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                if (t % 9 == 0):  #occasional print\n",
    "                    print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                sumPartialWedgePops = 0. \n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = avgDistrictPop / nWedges  #at beginning, all wedges are active\n",
    "                #latestWedgeDensity = [0.]*nWedges \n",
    "                wedgeComplete = [\"No\"]*nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = currentR   #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                wedgeTotGOP = [0.]*nWedges\n",
    "                wedgeHisp = [0.]*nWedges\n",
    "                wedgeBlack = [0.]*nWedges\n",
    "                wedgeTotVote = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                angle[0] = startAngle - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(1,nWedges,1) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = startAngle - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                    wedgeHisp[nW] = tractHisp[t]/tractPop[t]*wedgePop[nW]\n",
    "                    wedgeBlack[nW] = tractBlack[t]/tractPop[t]*wedgePop[nW]\n",
    "                    #latestWedgeDensity[nW] = 2. * tractPop[t] / tractArea[t]   #the \"2\" is a white lie for stability\n",
    "                HDpop = np.sum(wedgePop)   #END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if(tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,   and reduce gain\n",
    "            print(\"loop no {0} for tract no {1} with population {2}\".format(tractLoopCounter[t],t,HDpop) )\n",
    "            gain = max(0.4,gain*0.9)  #attenuate gain, but no lower than 0.4 so we can converge in <20 iterations\n",
    "        if (tractLoopCounter[t] == nLoopFix and nActiveWedges > 1) : #find and shut off the least populated \"active\" wedge\n",
    "            problemWedgeNo = 0\n",
    "            maxOffPop = 0.\n",
    "            for nW in range(nWedges):\n",
    "                if isOverEdge[nW]==0 and targetWedgePop-wedgePop[nW] > maxOffPop  : #Is this worst active wedge so far?\n",
    "                    problemWedgeNo = nW   #NOTE - above line used to be abs(x)  Let's not stop overshot wedges\n",
    "                    maxOffPop = abs(targetWedgePop - wedgePop[nW])  #update worst differential\n",
    "            isOverEdge[problemWedgeNo] = 1  #finished loop, ID'd worst wedge.  Cut it off.\n",
    "            nActiveWedges -= 1\n",
    "            sumPartialWedgePops += wedgePop[problemWedgeNo]\n",
    "            targetWedgePop = (avgDistrictPop - sumPartialWedgePops )/nActiveWedges \n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,guessedR, wedgePop, overEdge?\",nWW,str(round(oldR[nWW],5)), \n",
    "                      str(round(guessedR[nWW],5)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    for nPP in range (nPrecincts) :\n",
    "        precinctUse[nPP] += loopPrecinctUse[nPP]\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0):  #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDvGOP[t] = 0.5  #a white lie\n",
    "        HDvHisp[t] = 0.5 #a white lie   #np.sum(wedgeHisp)/np.sum(wedgePop) #div by zero\n",
    "        HDvBlack[t] = 0.5\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)\n",
    "        HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)\n",
    "        HDvGOP[t] = np.sum(wedgeTotGOP)/np.sum(wedgeTotVote)\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+oldR[0]*math.cos(angle[0]), tractCP.y+oldR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+oldR[0]*math.cos(angle2[0]),tractCP.y+oldR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(1,nWedges):\n",
    "            outerPt2 = Point(tractCP.x+oldR[nW]*math.cos(angle[nW]), tractCP.y+oldR[nW]*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+oldR[nW]*math.cos(angle2[nW]),tractCP.y+oldR[nW]*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area #would need to track wedge shapes better to compute a final area \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/statePop ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/statePop ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000007372524545\n",
      "Average and max number of wedgePop loops per tract were:  6.589015151515151 30.0\n",
      "min,average,max of Home District areas were:  0.0 0.7320280846051762 2.7538489372295043 for  OH\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start 8:04pm 2/4 gain=0.8 (gain=0.6 has some high loop counts), 700 by 8:37, 1640 by 9:40, done by 11:00\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OH\n"
     ]
    }
   ],
   "source": [
    "print(STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "# now convert output to pandas dataframe and export\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"tractNo\":tractNo,\"Loops\":tractLoopCounter,\n",
    "                    \"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\"centroid x\":tractCPx,\n",
    "                    \"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\"wrongPop\":wrongPop,\n",
    "                    \"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+\"_nD\"+str(nDistricts)+\"tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[t]=t\n",
    "    vtdX[t] = vtdGeom[t].centroid.x\n",
    "    vtdY[t] = vtdGeom[t].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+\"VTDstats_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "234 1.01 ( -81.49945382197905 40.30106644753571 ) 0.7132443701660767 t, pop/target,(x,y), pctR\n",
      "527 1.05 ( -83.55836150407727 41.56755224378397 ) 0.5218419387280113 t, pop/target,(x,y), pctR\n",
      "528 1.02 ( -83.47811869884498 41.53502827616783 ) 0.5326047495900768 t, pop/target,(x,y), pctR\n",
      "888 1.03 ( -84.41100807102421 39.18118629677892 ) 0.43003455918336647 t, pop/target,(x,y), pctR\n",
      "1004 1.09 ( -83.06289401316428 41.2156658602856 ) 0.5786556917895287 t, pop/target,(x,y), pctR\n",
      "1426 1.05 ( -80.77026626678946 41.22672634559323 ) 0.5767001338226395 t, pop/target,(x,y), pctR\n",
      "2043 1.07 ( -84.59436225101324 39.29036979495837 ) 0.5305966948942861 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=12)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = MAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "15cb6aae-0cb8-4bb5-a81c-098c01dfe25c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting extreme non-convergers  #not needed for Ohio\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if ratio < 0.6 or ratio > 1.5 :      \n",
    "            print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = MAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 :\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[t].centroid.x\n",
    "        tractY = vtdGeom[t].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[t].centroid.x, vtdGeom[t].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[t] == 0 :\n",
    "            x,y = vtdGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[t].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = MAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "e98d9bf6-3bfd-45de-8ed6-2b5b723aaa2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished looping on redo of tract  167\n",
      "finished looping on redo of tract  1454\n",
      "finished looping on redo of tract  1497\n",
      "finished looping on redo of tract  1622\n",
      "finished looping on redo of tract  2224\n",
      "finished looping on redo of tract  2230\n",
      "finished looping on redo of tract  2252\n",
      "finished looping on redo of tract  2257\n",
      "finished looping on redo of tract  4635\n"
     ]
    }
   ],
   "source": [
    "# REDO OF HOME DISTRICT CODE FOR SELECTED TRACTS THAT DID NOT CONVERGE - updated 2/3/22 with Texas adds, may not be ready yet\n",
    "#redo = [  ,   ,]   #see above for how we selected the tract NUMBERS TO REDO HERE\n",
    "\n",
    "#minTractPop = 100  #this is usually set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon\n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "guessedR = [0.]*nWedges  #wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "\n",
    "targetWedgePop = avgDistrictPop / nWedges\n",
    "toler = 0.01  #adjustable - fractional slop in district population.  normally 0.01\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 8  #at this loop number, we alert user of problem\n",
    "nLoopFix = 18 #at this loop number, we will shut off a low-pop active wedge\n",
    "nGiveUp = 30\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.6  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "globalMax_dr = 0.5*(MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "printAngle = [45.] * nTracts  #for debugging, tracking the exit angle.  Set at 45 if not triggered\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8  #fraction of pi for maxWideAngle\n",
    "\n",
    "nRedos = len(redo)\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "guessedR = [0.]*nWedges  #wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgeAngle = 2*pi / nWedges\n",
    "wedgePop = [0.]*nWedges\n",
    "redotractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "redonearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "#tractUse = [0.] * nTracts  #for redos, do not track usage -- cannot correct this\n",
    "#precinctUse = [0.] * nPrecincts   \n",
    "redoHDvPop = [0.]*nRedos\n",
    "redoHDvGOP = [0.]*nRedos  #GOP lean of each tract's \"home district\"\n",
    "redoHDvHisp = [0.]*nRedos\n",
    "redoHDvBlack = [0.]*nRedos\n",
    "redoHDweight = [0.]*nRedos  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "redoHDarea = [0.]*nRedos  #***NOT YET VETTED geographical area of each home district\n",
    "targetWedgePop = avgDistrictPop / nWedges\n",
    "toler = 0.01  #adjustable - fractional slop in district population.  normally 0.01\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 8  #at this loop number, we alert user of problem\n",
    "nLoopSuperPrint = 20 # at this loop number, we print more details\n",
    "nLoopFix = 14 #at this loop number, we will shut off a low-pop active wedge\n",
    "nGiveUp = 30\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.5  #adjustable - start low for these problem tracts\n",
    "tractPrintInterval = 24  #for tracking progress\n",
    "minTractArea = 0.00001 * origMAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "\n",
    "for redoNumber in range(nRedos) :  #loop only on tracts specified in line at beginning of this block\n",
    "    t = redo[redoNumber]\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    redotractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # estimated radius of densest district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    guessedR = [ (avgDistrictPop / homeGridDensity / pi)**0.5 ]*nWedges  #*** IS THIS EVEN USED?  CORRECT?\n",
    "    randomAngle = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.\n",
    "    sumPartialWedgePops = 0.  #this will track total pop of wedges that ran into a boundary before meeting quota\n",
    "    nActiveWedges = nWedges\n",
    "    targetWedgePop = avgDistrictPop / nWedges  #at beginning, all wedges are active\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgeComplete = [\"No\"]*nWedges\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    currentR = [tinyR]*nWedges\n",
    "    old_dr = [tinyR]*nWedges   #this will track the last radial step (to help convergence)\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    wedgeTotGOP = [0.]*nWedges\n",
    "    wedgeHisp = [0.]*nWedges\n",
    "    wedgeBlack = [0.]*nWedges\n",
    "    wedgeTotVote = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+randomAngle   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #prevent rare div-by-zero\n",
    "        if tractPop[t] > minTractPop : #skip minorities for negligible tracts\n",
    "            wedgeHisp[nW] = tractHisp[t]/tractPop[t]*wedgePop[nW]\n",
    "            wedgeBlack[nW] = tractBlack[t]/tractPop[t]*wedgePop[nW]\n",
    "        # prior to entering while loop, need to pretend we had been through the loop and gotten below two values\n",
    "        #oldR[nW] = tinyR\n",
    "        # latestWedgeDensity[nW] = 2. * tractPop[t] / tractArea[t]   #the \"2\" is a white lie to keep first guessed R low\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0): #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            # wedgeTriAR[nW] = math.sin(0.5*wedgeAngle[nW])*math.cos(0.5*wedgeAngle[nW]) #triangle area ratio shortcut\n",
    "            #print(t,nW,round(wedgeTriAR[nW],4),\"are tract, wedge no, wedge area ratio\")\n",
    "            if isOverEdge[nW] != 1:    #...except we skip underpopulated wedges with no room to grow\n",
    "                neededWedgePop = targetWedgePop - wedgePop[nW]  #note that target is not updated until all nW looped\n",
    "                R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]\n",
    "                if (abs(wedgePopDelta) < 0.001 ):  #avoid divide-by-zero in sparse areas\n",
    "                    wedgePopDelta = 0.001  #assume we need to grow if we didn't pick up or lose much pop in last loop\n",
    "                guessedR2 = max(tinyR*tinyR,currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop)\n",
    "                guessed_dr = guessedR2 ** 0.5 - currentR[nW]\n",
    "                tentative_dr = gain * guessed_dr   #gain typically ~0.7 for stability\n",
    "                if (tentative_dr > max_dr[nW]):  #in danger of taking too big of a positive step\n",
    "                    tentative_dr = max_dr[nW]\n",
    "                if (tentative_dr < -0.3 * max_dr[nW]):\n",
    "                    tentative_dr = -0.3 * max_dr[nW]\n",
    "                    if old_dr[nW] > 0 :  #we are yo-yoing; this step negative but last step was positive\n",
    "                        max_dr[nW] = 0.5*max_dr[nW]  #reduce future step size                        \n",
    "                dr = tentative_dr\n",
    "                old_dr[nW] = dr  #for next time around\n",
    "                #WP = round(wedgePop[nW],2)\n",
    "                #oWP = round(oldWedgePop[nW],2)\n",
    "                #nWP = round(neededWedgePop,3)\n",
    "                #curR = round(currentR[nW],2)\n",
    "                #g_dr = round(guessed_dr,2)\n",
    "                #if (nW == 0):\n",
    "                    #print(targetWedgePop,\" (tWP),     tract, loop, wedgeNo, wedgePop, oWP,nWP currR, gues_dr, dr,R2delta\")\n",
    "                #print(t,int(tractLoopCounter[t]),nW,WP,oWP,nWP,curR,g_dr, dr, round(R2delta,3))\n",
    "                # current values are now \"old\" values ...\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge piece will be subtracted from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                latestWedgeHisp = 0.\n",
    "                latestWedgeBlack = 0.\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    latestWedgeBlack += fracArea*tractBlack[nTT]\n",
    "                                    latestWedgeHisp += fracArea*tractHisp[nTT]\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        # now do precincts\n",
    "                        for pp in range(nGridPrecincts[nG]):  #same comments as above loop, but now for precincts to get R lean\n",
    "                            nPP = gridPrecinctNo[nG][pp]\n",
    "                            if usedPrecinct[nPP] == 0  :\n",
    "                                usedPrecinct[nPP] = 1\n",
    "                                overlap = vtdGeom[nPP].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/vtdArea[nPP]\n",
    "                                    wedgeTotGOP[nW] += np.sign(dr)*fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                                    wedgeTotVote[nW] += np.sign(dr)*fracArea*vtdPop[nPP]\n",
    "                                    loopPrecinctUse[nPP] += np.sign(dr)*overlap/vtdArea[nPP] * tractPop[t]/avgDistrictPop\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                wedgeHisp[nW] +=np.sign(dr)*latestWedgeHisp\n",
    "                wedgeBlack[nW] +=np.sign(dr)*latestWedgeBlack\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop : #if we plan to increase wedge size, stop if we're beyond buffered boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    # alt to below to try: if leadingEdge.centroid.intersects(bufferedMAP)\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #just sayin....\n",
    "                    else:  #our wedge is fully beyond the map.  since map is fairly convex, give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        wedgePopGap[nW] = avgDistrictPop/nWedges - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1\n",
    "                        sumPartialWedgePops += wedgePop[nW]\n",
    "                        if (nActiveWedges == 0) : #we're doomed\n",
    "                            redotractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                        \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        redotractLoopCounter[t] +=1   # still looping on home district Pop\n",
    "        targetWedgePop = (avgDistrictPop - sumPartialWedgePops )/nActiveWedges  #active wedges to pick up any slack\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #we adjust wedge angles at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                shortW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the 2nd shorted wedge\n",
    "                if (isOverEdge[shortW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            shortW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[shortW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    wedgeAngle=[avgWedgeAngle]*nWedges\n",
    "            else: # a single shorted wedge (over boundary).  Completely re-start wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",redotractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                startAngle = exitAngle #                \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation   # *********************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0.\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                if (t % 9 == 0):  #occasional print\n",
    "                    print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                sumPartialWedgePops = 0. \n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = avgDistrictPop / nWedges  #at beginning, all wedges are active\n",
    "                #latestWedgeDensity = [0.]*nWedges \n",
    "                wedgeComplete = [\"No\"]*nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = currentR   #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                wedgeTotGOP = [0.]*nWedges\n",
    "                wedgeHisp = [0.]*nWedges\n",
    "                wedgeBlack = [0.]*nWedges\n",
    "                wedgeTotVote = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                angle[0] = startAngle - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(1,nWedges,1) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = startAngle - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                    wedgeHisp[nW] = tractHisp[t]/tractPop[t]*wedgePop[nW]\n",
    "                    wedgeBlack[nW] = tractBlack[t]/tractPop[t]*wedgePop[nW]\n",
    "                    #latestWedgeDensity[nW] = 2. * tractPop[t] / tractArea[t]   #the \"2\" is a white lie for stability\n",
    "                HDpop = np.sum(wedgePop)   #END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if(redotractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,   and reduce gain\n",
    "            print(\"loop no {0} for tract no {1} with population {2}\".format(redotractLoopCounter[t],t,HDpop) )\n",
    "            gain = max(0.4,gain*0.9)  #attenuate gain, but no lower than 0.4 so we can converge in <20 iterations\n",
    "        if (redotractLoopCounter[t] == nLoopFix and nActiveWedges > 1) : #find and shut off the least populated \"active\" wedge\n",
    "            problemWedgeNo = 0\n",
    "            maxOffPop = 0.\n",
    "            for nW in range(nWedges):\n",
    "                if isOverEdge[nW]==0 and targetWedgePop-wedgePop[nW] > maxOffPop  : #Is this worst active wedge so far?\n",
    "                    problemWedgeNo = nW   #NOTE - above line used to be abs(x)  Let's not stop overshot wedges\n",
    "                    maxOffPop = abs(targetWedgePop - wedgePop[nW])  #update worst differential\n",
    "            isOverEdge[problemWedgeNo] = 1  #finished loop, ID'd worst wedge.  Cut it off.\n",
    "            nActiveWedges -= 1\n",
    "            sumPartialWedgePops += wedgePop[problemWedgeNo]\n",
    "            targetWedgePop = (avgDistrictPop - sumPartialWedgePops )/nActiveWedges \n",
    "        if (redotractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,guessedR, wedgePop, overEdge?\",nWW,str(round(oldR[nWW],5)), \n",
    "                      str(round(guessedR[nWW],5)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(redotractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    # for nTT in range (nTracts) :         #not tracking these for \"redo\" code\n",
    "    #    tractUse[nTT] += loopTractUse[nTT]\n",
    "    # for nPP in range (nPrecincts) :\n",
    "    #    precinctUse[nPP] += loopPrecinctUse[nPP]\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0):  #we bypassed the big loop; this tract is inconsequential\n",
    "        redoHDvPop[t] = HDpop  #a white lie\n",
    "        redoHDvGOP[t] = 0.5  #a white lie\n",
    "        redoHDvHisp[t] = 0.5 #a white lie   #np.sum(wedgeHisp)/np.sum(wedgePop) #div by zero\n",
    "        redoHDvBlack[t] = 0.5\n",
    "        redoHDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        redoHDvPop[t] = np.sum(wedgePop)\n",
    "        redoHDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)\n",
    "        redoHDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)\n",
    "        redoHDvGOP[t] = np.sum(wedgeTotGOP)/np.sum(wedgeTotVote)\n",
    "        redoHDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+oldR[0]*math.cos(angle[0]), tractCP.y+oldR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+oldR[0]*math.cos(angle2[0]),tractCP.y+oldR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(1,nWedges):\n",
    "            outerPt2 = Point(tractCP.x+oldR[nW]*math.cos(angle[nW]), tractCP.y+oldR[nW]*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+oldR[nW]*math.cos(angle2[nW]),tractCP.y+oldR[nW]*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area #would need to track wedge shapes better to compute a final area \n",
    "\n",
    "    # end of loop on this tract\n",
    "    print(\"finished looping on redo of tract \",t)    \n",
    "for redoNumber in range(nRedos):\n",
    "    t = redo[redoNumber]\n",
    "    if(wrongPop[t] > 0):\n",
    "        redoHDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "#HDsumWeight = np.sum(HDweight)\n",
    "#print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "#print(\"Average and max number of wedgePop loops per tract were: \",np.average(redotractLoopCounter),np.max(redotractLoopCounter) )\n",
    "#print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "#stateGOP2 = 0.\n",
    "#stateHisp2 = 0.\n",
    "#stateBlack2 = 0.\n",
    "#for t in range(nTracts):  #skip these stats for redo's\n",
    "    #redoHDweight[t] = redoHDweight[t]/HDsumWeight   #renormalizing not needed for redo's\n",
    "    #stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    #stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    #stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "#statePop = np.sum(tractPop)\n",
    "#print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "#print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/statePop ) )\n",
    "#print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/statePop ) )\n",
    "#print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "c643cfce-2ba0-400a-b058-38b70a1e7309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "167 0.42670940772445526 0.5959358888046177 3304508.9080142053 773842.8326444986 t, pct R, HDpop (old, new)\n",
      "1454 0.41524448097299915 0.5800145474364335 1172293.3282999888 774541.1779883557 t, pct R, HDpop (old, new)\n",
      "1497 0.4770602556336929 0.5795536593965854 826449.7079291432 775793.0626293202 t, pct R, HDpop (old, new)\n",
      "1622 0.4298238380540196 0.6092131361046392 4376161.765201239 766745.1475002962 t, pct R, HDpop (old, new)\n",
      "2224 0.4202597030530061 0.5668144519381301 2327174.28023302 773170.8736317176 t, pct R, HDpop (old, new)\n",
      "2230 0.5575614036130331 0.542209035609453 852197.9431255059 774697.8700699711 t, pct R, HDpop (old, new)\n",
      "2252 0.536541402878683 0.6044275995196078 1081413.1747042637 775452.8477253963 t, pct R, HDpop (old, new)\n",
      "2257 0.3914148669815983 0.5879586890427229 3219967.8158555385 763750.1676691435 t, pct R, HDpop (old, new)\n",
      "4635 0.4496474695111592 0.5477170441527741 3447489.674097435 773381.8037149538 t, pct R, HDpop (old, new)\n"
     ]
    }
   ],
   "source": [
    "for r in range(nRedos):\n",
    "    t = redo[r]       \n",
    "    print(t,HDvGOP[t], redoHDvGOP[r], HDvPop[t], redoHDvPop[r],\"t, pct R, HDpop (old, new)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "054bc1a4-0533-4a42-b86f-f8c47173edae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "167 1.0335348430384217 1.4729084821699023 t, HDarea (old, new)\n",
      "1454 0.9558656673550292 1.5108109325676395 t, HDarea (old, new)\n",
      "1497 0.9320967483217852 0.5948285353685191 t, HDarea (old, new)\n",
      "1622 0.8794208778688568 1.4427634762109514 t, HDarea (old, new)\n",
      "2224 1.0722087967619973 0.715291493488541 t, HDarea (old, new)\n",
      "2230 1.4209137565691847 1.5915292621864652 t, HDarea (old, new)\n",
      "2252 1.260114833268251 1.1913415404889673 t, HDarea (old, new)\n",
      "2257 1.2965261171311089 1.546867487643159 t, HDarea (old, new)\n",
      "4635 0.6631426919693022 0.7404683088835482 t, HDarea (old, new)\n"
     ]
    }
   ],
   "source": [
    "for r in range(nRedos):\n",
    "    t = redo[r]       \n",
    "    print(t,HDarea[t], redoHDarea[r],\"t, HDarea (old, new)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = MAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used less than 0.85 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.85 usage)\n",
    "maxPlot = 0.85\n",
    "print(\"here is a map of tracts that were used less than {0} of expectation\".format(maxPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = origMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.15 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.15\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 2 tracts that were used more than 1.5 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.5\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of TRACT usage by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram  (this is for wedge, not NN approach)\n",
    "n_bins=50\n",
    "print(\"this is a histogram of TRACT usage by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of PRECINCT usage by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "print(\"this is a histogram of PRECINCT usage by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops as a histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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PAieq6gPLfjSqfXq+dY5tnyaZS/Lq4f7LgbcCX2HG+3Pdf4sPYPgK5wf5/8sqHVjbGc1GktewdNQES5ep+uuxrDXJJ4C3sPTXFJwB3gP8LXAE+BHgceCmqlrXXzA4zzrfwtKpoAIeA377ufP661WSnwf+GfgS8N1h+N0sfT4zmn16gXW+nRHt0yQ/xdKXIDaxdGBzpKr+OMkPMcP9OYpASZLGZwyn+CRJI2SgJEktGShJUksGSpLUkoGSJLVkoCRJLRkoSVJL/ws+seEcMMM0NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here's a look at numerical stability - number of loops as a histogram\")\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote should have been 0.54077 but HD average is 0.54211\n"
     ]
    },
    {
     "data": {
      "image/png": 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o8LJwjC7v/X1VfXED62nBgJqeIUtG/Z+qejjJdyW5tqraLRL5Kgzph93A8XE4XQvckuRiVX1iKhVOx5r9UFVfXvbzySR/OKfnwzngC1X1FeArSR4G3gTMUkCt5/3hIHNweQ+8xDdNay77lOSNGb8rj7/08WrgP6Ze6cZasx+q6saqWqyqReAjwHtnLJxg2PnwHcvOhz2M/l7n7nwA/gL40SRXjS9vvRU4M+U6N9qgZeGSfAvw44z6ZOY5gpqSGrZk1M8wWtPwq8B/AT+7bNLETBjYDzNvYD+8G/jlJBcZnQ8H5/F8qKozSf4KeBL4OvDBqnpq86qevHX8XbwL+OvxaHLmudSRJKklL/FJkloyoCRJLRlQkqSWDChJUksGlCSpJQNKktSSASVJaul/AOTEsKvSnDWtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote should have been\",round(stateGOP,5),\"but HD average is\",round(stateGOP2,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.04371884099746297, should have been 0.04418071082647256\n",
      "this is a histogram of home-district pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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JUpcMKElSl5oCKsmRJDeSLCY5u8r5JHl+OH8tyaFJtUn+cpKPJPn94b+Pbc6UJEnTYGJAJdkBvAAcBQ4CTyc5uKLbUWB2eJ0CzjXUngV+rapmgV8bjiVJAmBnQ58ngcWqugmQ5BJwHPjkWJ/jwItVVcCVJLuTPAHsX6P2OPCNQ/1/BH4DeN8G5yM9NPvPfnjTPuvV575t0z5LelS1BNQe4NbY8RLwVEOfPRNqv7KqbgNU1e0kb1nty5OcYnRVBvB/k9xoGPOWyI9u6sc9DnxhUz9xuk31evlr66FyvdZnM9brr63W2BJQWaWtGvu01K6pqi4AF9ZT8yhKslBVcw97HI8K16uda7U+rtf6vJnr1fKQxBKwb+x4L/BaY5+1aj833AZk+O/n24ctSZp2LQF1FZhNciDJLuAEML+izzzwzPA032HgznD7bq3aeeC9w/v3Ar+0wblIkqbIxFt8VXU/yRngJWAHcLGqric5PZw/D1wGjgGLwF3g5Fq1w0c/B/xckh8EPgN856bO7NEz9bcxN5nr1c61Wh/Xa33etPXK6ME7SZL64k4SkqQuGVCSpC4ZUFuoYcuo7x22irqW5LeSfN3DGGcvJq3XWL9vSPKnSb5jK8fXm5b1SvKNSV5Ocj3Jb271GHvS8P/jX0ryX5J8fFivkw9jnD1IcjHJ55O88oDzD9zubkOqytcWvBg9JPIHwF8HdgEfBw6u6PO3gMeG90eB337Y4+55vcb6/TqjB3W+42GPu+f1AnYz2sXlq4bjtzzscXe+Xv8C+NHh/Qzwv4FdD3vsD2m9/g5wCHjlAeePAb/C6GdfD2/W711eQW2dL20ZVVX3gC9u+/QlVfVbVfV/hsMrjH5ubLuauF6DHwZ+AX+OrmW9vgf4YFV9BqCqtvOataxXAX8xSYC/wCig7m/tMPtQVR9lNP8H+dJ2d1V1BfjidncbYkBtnQdtB/UgP8joTyTb1cT1SrIHeA9wfgvH1auWX19vAx5L8htJPpbkmS0bXX9a1uungL/JaHOBTwD/uKpe35rhPXLW+/tbk5atjrQ5mrd9SvJNjALqb7+pI+pby3r9G+B9VfWnoz/kbmst67UT+HrgW4AvA/57kitV9Xtv9uA61LJe3wq8DHwz8FbgI0n+W1X90Zs8tkfRhre1W40BtXVatowiyduBnwaOVtX/2qKx9ahlveaAS0M4PQ4cS3K/qn5xS0bYl9Ytyb5QVX8M/HGSjwJfB2zHgGpZr5PAczX6S5bFJP8D+Brgd7ZmiI+Upt/f1stbfFtn4pZRSb4K+CDwfdv0T7XjJq5XVR2oqv1VtR/4eeAfbdNwgrYtyX4JeGeSnUm+nNG/LPCpLR5nL1rW6zOMrjZJ8pXAVwM3t3SUj44HbXe3IV5BbZFq2zLqA8BXAP9uuCq4X9t0V+XG9dKgZb2q6lNJ/itwDXgd+OmqWvWx4WnX+OvrR4D/kOQTjG5hva+qtuU/w5HkZxn9+32PJ1kC/iXwZ2Ht7e42/L3DI4KSJHXFW3ySpC4ZUJKkLhlQkqQuGVCSpC4ZUJKkLhlQkqQuGVCSpC79P9ius2bzJBWmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(stateHisp2, np.sum(tractHisp)/np.sum(tractPop) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by pct Hispanic for OH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 10\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.40\n",
    "minHisp2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        if (HDvHisp[t] > minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by pct Black for OH\n"
     ]
    },
    {
     "data": {
      "image/png": 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LYP39g4DzVrruMfT594G9mueHT3qf2/Z73nFfBD4DPGWl6x7Dn/WewDeBfZvXt13pusfU778G3tA8nwJ+Buy60rUP2e+HA4cAlyzy+ciybFJG8G22PXgi8N4aOBfYM8ntx13oCC3Z56o6p6p+3rw8l8G1BpOu7RYXxwIfA64aZ3EdadPnpwMfr6rvA1TVaul3AbsnCXBLBgG/cbxljlZVfYVBPxYzsiyblIC/I/CDea+vbN7b3mMmyfb253kM/tWfdEv2O8kdgT8E3jHGurrU5s/694C9knw5ybokzxpbdd1p0++3APdgcJHkxcBxVXXDeMpbMSPLskm54UebbQ9abY0wQVr3J8mhDAL+oZ1WNB5t+n0i8Mqq2jQY2E28Nn3eGbgf8CjgZsDXkpxbVf/ddXEdatPvPwDWA48E7gKcleSrVXVNx7WtpJFl2aQEfJttD/q2NUKr/iS5D/BO4PCq+umYautSm37PAB9qwn1v4IgkG6vqE2OpcPTa/v2+uqquA65L8hXgIGCSA75Nv48GTqjB5PR3klwO3B04fzwlroiRZdmkTNG02fbgU8Czml+gHwT8sqp+PO5CR2jJPifZF/g48MwJH8nNt2S/q2q/qpquqmngo8AxExzu0O7v9yeBhyXZOcnNgQcCG8Zc56i16ff3Gfy/FpLcjsGOs5eNtcrxG1mWTcQIvhbZ9iDJC5vP38FgNcURwHeA6xn8yz+xWvb51cBtgLc1o9mNNeE78LXsd6+06XNVbUhyJnARcAPwzqpacJndpGj5Z/13wLuTXMxg6uKVVTXR2wgn+SDwCGDvJFcCrwF2gdFnmVsVSFJPTcoUjSRpOxnwktRTBrwk9ZQBL0k9ZcBLUk8Z8Fq1khyc5IhFPntEkl82uzdelOQLSW7bfPacJG9ZZpu/GqZmaXsY8FrNDmaw3ngxX62qg6vqPgwuynnRWKqSRsSAV28kmU7yrSTvaUbdH22u+ty8d/w5zb7i5ye5FfA64KnNKP2p2zhvgN2Bny/w2eOTnJfkG80o/3bN+7dMcmqSi5tanrzV9/ZO8rUkR47yfwNpPgNefXMAsLYZdV8DHNNcBv9hBjsRHgQ8GriOwZXAH25G6R9e4FwPS7KeweXyjwZOWeCYs4EHVdV9GWx3+4rm/b9hcIn5vZtavrj5C80/AmcAr66qM4busbQIA15984Oq+q/m+fsZ7LB5APDjqvo6QFVdU1Vt9hTfPEVzJ+BU4I0LHLMP8LnmUvq/BO7ZvP9o4K2bD5q3b/8uwH8Ar6iqs7ava9L2MeDVN1vvvVEM9jAZdk+OTzG4E8/WTgbeUlX3Bl4A7Na8v1ibG4F1DLbBlTplwKtv9k3y4Ob5UQymUL4F3CHJ/QGS7J5kZ+BaBnPrbTyUwe3ltnYr4IfN82fPe//zwIs3v0iyV/O0gOcCd8827jcrjYIBr77ZADw7yUXArYG3N7eDeypwcpILgbMYjLS/BBy4jR9ZH9Z8diHwTODlCxzzWuAjSb4KzN/l8O8Z3IHpkub7h27+oKo2Mdga99AkxwzZX2lR7iap3kgyDXy6qu610rVIOwJH8JLUU47gJamnHMFLUk8Z8JLUUwa8JPWUAS9JPWXAS1JP/T+/UTchJtIgGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black-heavy home districts?\n",
    "minBlack1 = 0.30\n",
    "minBlack2 = 0.40\n",
    "for t in range(nTracts):\n",
    "    if (HDvBlack[t] > minBlack1 and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] > minBlack2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='black' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  786629.8666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "50bd59d3-cc09-4273-881c-ada4c8472bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6665616912462334\n"
     ]
    }
   ],
   "source": [
    "#print(len(voteData))\n",
    "# let's confirm our expected seats in above graph is correct\n",
    "seats1=0.\n",
    "for t in range (nTracts):\n",
    "    if(HDvGOP[t] > 0.500 ):\n",
    "        seats1 += HDweight[t]\n",
    "print(seats1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7e3774e6-1c82-41f0-b3d9-84017d8eed9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "constant statewide, working on home district number  0\n",
      "constant statewide, working on home district number  200\n",
      "constant statewide, working on home district number  400\n",
      "constant statewide, working on home district number  600\n",
      "constant statewide, working on home district number  800\n",
      "constant statewide, working on home district number  1000\n",
      "constant statewide, working on home district number  1200\n",
      "constant statewide, working on home district number  1400\n",
      "constant statewide, working on home district number  1600\n",
      "constant statewide, working on home district number  1800\n",
      "constant statewide, working on home district number  2000\n",
      "constant statewide, working on home district number  2200\n",
      "constant statewide, working on home district number  2400\n",
      "constant statewide, working on home district number  2600\n",
      "constant statewide, working on home district number  2800\n",
      "constant statewide, working on home district number  3000\n",
      "variable statewide, working on bin number  0\n",
      "variable statewide, working on bin number  200\n",
      "variable statewide, working on bin number  400\n",
      "variable statewide, working on bin number  600\n",
      "variable statewide, working on bin number  800\n",
      "hd-based smeared statewide vote was 0.542116 (from mean of 0.542105), expected was 0.5407669764770272\n",
      "fSum, f2sum, dV were  0.9999989752939275 0.9999989752938585 0.001\n",
      "exp seat fraction is 0.6498413528614261 (or 0.645586362955781 if we include state outcome variance) \n",
      "responsiveness for the map = 2.5294664987416735 using district variance 0.05 and statewide variance 0.03\n"
     ]
    }
   ],
   "source": [
    "# THIS BLOCK: COMPUTING FINAL STATS with FRACTIONAL-SEAT SMEARING (about 2min for 3200 tracts)\n",
    "#  NEED ** TO ** REWORK FROM VOTES2SEATS CODE ***\n",
    "nHDs = len(HDweight)\n",
    "seatVar = 0.05  #this is the normal variance in the outcome of a district race\n",
    "stateVar = 0.03  #this is the normal variance in the statewide vote (to account for blue- and red-wave years)\n",
    "\n",
    "maxSD = 3.1  #we truncate normal distributions more than 3.1 sigma away (0.1% tail on either side)\n",
    "#nBins = xxx, dV = 1./nBins  #passed from above sections\n",
    "#nTracts = ..., nHDs = nTracts    #**(passed) this will be passed from above code\n",
    "f_V = [0.]*nBins  #binned votes curve with fractional seats\n",
    "f2_V = [0.]*nBins #binned votes curve with variation in statewide outcome\n",
    "totalF = 0.  #for normalizing f(V)\n",
    "stateAvg1 = 0.\n",
    "printInterval = 200 #for tracking our progress\n",
    "HDsum = np.sum(HDweight)\n",
    "for hd in range(nHDs):\n",
    "    HDweight[hd] = HDweight[hd] / HDsum  #normalize home district weights before we start\n",
    "    stateAvg1 += HDweight[hd]*HDvGOP[hd]  #first pass on hd-based statewide vote (no fractional seat smearing)\n",
    "    \n",
    "for hd in range(nHDs):   #votes curve to seats curve first loop: statewide vote is constant  \n",
    "    if hd % printInterval == 0 :\n",
    "        print(\"constant statewide, working on home district number \",hd) \n",
    "    HDv = HDvGOP[hd] #shorthand for GOP vote in home district number = hd\n",
    "    lowBin =  max(0, int(round( (HDv-maxSD*seatVar)/dV, 0)) )   #b.c. convolution for bins below v = vLow is negligible\n",
    "    highBin = min(nBins-1, int(round( (HDv+maxSD*seatVar)/dV, 0)) )\n",
    "    vLow =  dV * lowBin\n",
    "    vHigh = dV * highBin  \n",
    "    f_V[lowBin] +=  HDweight[hd]*norm.cdf((vLow+0.5*dV - HDv)/seatVar)      #truncate left tail, assign to leftmost bin in this group\n",
    "    f_V[highBin] += HDweight[hd]*(1-norm.cdf((vHigh-0.5*dV - HDv)/seatVar)) #and assign all of right tail to rightmost bin in this group\n",
    "    for bin in range(lowBin+1,highBin,1) : #only convolute between these tails\n",
    "        v = dV * (bin + 0.5)  #mean vote is in middle of bin range\n",
    "        lowD = v - 0.5*dV - HDv  #difference between left edge of bin and home-district's GOP lean\n",
    "        hiD = v + 0.5*dV - HDv   #same, right edge\n",
    "        # delta = v - HDv  #signed difference between middle of this vote interval bin and h.d. GOP lean   \n",
    "        f_V[bin] += HDweight[hd]*( norm.cdf(hiD/seatVar)-norm.cdf(lowD/seatVar) )\n",
    "        # fracOfHDinthisVinterval = same as above\n",
    "        # print(HDv,v,dV,fracOfHDinthisVinterval,\"HDV,v,dV,fracOfHDinthisVinterval\")\n",
    "\n",
    "stateAvg2 = 0.     #let's check the statewide vote\n",
    "for bin in range(nBins):  \n",
    "    if bin % printInterval == 0 :\n",
    "        print(\"variable statewide, working on bin number \",bin) \n",
    "    stateAvg2 += f_V[bin]*((bin+0.5)*dV)\n",
    "        # now do binning for votes curve with statewide variation.  Approximation to rigorous but slow double-convoluting\n",
    "    binV = dV * (bin+0.5)\n",
    "    lowBin2 = max(0, bin - int(round(maxSD*stateVar/dV,0)) )   #again, left & right tail truncation\n",
    "    highBin2 = min(nBins-1,bin + int(round(maxSD*stateVar/dV,0)) )\n",
    "    vLow2 =  dV * (lowBin2+0.5)\n",
    "    vHigh2 = dV * (highBin2+0.5)\n",
    "    f2_V[lowBin2] +=  f_V[bin]*norm.cdf((vLow2+0.5*dV - binV)/stateVar)      #same truncation, but with statewide spread\n",
    "    f2_V[highBin2] += f_V[bin]*(1-norm.cdf((vHigh2-0.5*dV - binV)/stateVar))\n",
    "    for bin2 in range(lowBin2+1, highBin2, 1) : #only convolute between these tails\n",
    "        v = dV * (bin2 + 0.5)  #center of new bin\n",
    "        lowD = v - 0.5*dV - binV  #difference between left edge of new bin and old bin\n",
    "        hiD =  v + 0.5*dV - binV\n",
    "        f2_V[bin2] += f_V[bin]*( norm.cdf(hiD/stateVar)-norm.cdf(lowD/stateVar) )\n",
    "\n",
    "stateAvg1 = round(stateAvg1,6)\n",
    "stateAvg2 = round(stateAvg2,6)\n",
    "print(\"hd-based smeared statewide vote was {0} (from mean of {1}), expected was {2}\".format(stateAvg2, stateAvg1,stateGOP) )\n",
    "fSum = 0.\n",
    "f2Sum = 0.\n",
    "for bin in range (nBins) : #do a quick normalization, though these are close to unity\n",
    "    fSum += f_V[bin]\n",
    "    f2Sum += f2_V[bin]\n",
    "for bin in range (nBins):\n",
    "    f_V[bin] = f_V[bin]/fSum\n",
    "    f2_V[bin] = f2_V[bin]/f2Sum\n",
    "    \n",
    "expSeatFrac = 1.\n",
    "varSeatFrac = 1.\n",
    "print(\"fSum, f2sum, dV were \",fSum, f2Sum, dV)\n",
    "for bin in range (nBins) :\n",
    "    vMID = (bin + 0.5) * dV\n",
    "    vMin = vMID - 0.5 * dV\n",
    "    vMax = vMin + dV\n",
    "    weight = max(0,min( (0.5-vMin)/(vMax - vMin), 1 ) )   #use lever rule if this bin straddles the toss-up point\n",
    "    expSeatFrac -= weight * f_V[bin]\n",
    "    varSeatFrac -= weight* f2_V[bin]\n",
    "print(\"exp seat fraction is {0} (or {1} if we include state outcome variance) \".format(expSeatFrac,varSeatFrac) ) \n",
    "\n",
    "R = f2_V[int(nBins/2)]  / dV  #responsiveness is dS-dV at the toss-up point (after smoothing from state and local uncert'ys        \n",
    "print(\"responsiveness for the map = {0} using district variance {1} and statewide variance {2}\".format(R,seatVar,stateVar) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "1661890d-4bf0-4361-a57a-336189fc85b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribution of seats for state =  OH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S PLOT OUR OUTPUTS\n",
    "print(\"distribution of seats for state = \",STATE)\n",
    "vPlot = [0]*nBins\n",
    "for i in range(nBins) :\n",
    "     vPlot[i]=i*dV+0.5*dV\n",
    "seatRange = [0., np.max(f_V)]\n",
    "exp = [stateGOP,stateGOP]\n",
    "plt.plot(vPlot, f_V, label='S(V) at expd statewide vote')\n",
    "plt.plot(vPlot, f2_V,label=\"S(V) w varied statewide\")\n",
    "plt.plot(exp,seatRange)\n",
    "plt.xlabel('district vote')\n",
    "plt.ylabel('seat frequency')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6a8e043a-c560-4e06-a917-091d87a6fd3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT THE CUMULATIVE SEATS-VOTES CURVE, OVERLAY RESPONSIVENESS  #  **need to check this math**\n",
    "cumSeats = [0.]*nBins\n",
    "for bin in range(1,nBins-1):\n",
    "    cumSeats[bin]=cumSeats[bin-1]+ (0.5*f2_V[nBins-bin]+0.5*f2_V[nBins-bin-1] )\n",
    "cumSeats[nBins-1] = 1.\n",
    "\n",
    "minVote = stateAvg2 - stateVar\n",
    "maxVote = stateAvg2 + stateVar\n",
    "seatsX = [minVote, maxVote]\n",
    "minSeats = varSeatFrac + R* (minVote - 0.5)\n",
    "maxSeats = varSeatFrac + R* (maxVote - 0.5)\n",
    "seatsY = [minSeats,maxSeats]\n",
    "fifty50 = [0.50, 0.50]\n",
    "stringR = str(round(R,4))\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(vPlot, cumSeats, label='fraction of seats')\n",
    "plt.plot(seatsX,seatsY, label=\"respvness =\"+stringR)\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(.2,0.9,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9,color=\"red\")\n",
    "Ex = [0,stateGOP ]\n",
    "plt.grid()\n",
    "plt.xlabel('statewide vote')\n",
    "plt.ylabel('fraction of seats')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18767504-3fc9-4bef-9fb6-7ead3a92e3e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
